Reviewerly/exHarmony huggingface dataset:
Imagery in the entropic associative memory

Abstract The Entropic Associative Memory is a novel declarative and distributed computational model of associative memory. The model is general, conceptually simple, and offers an alternative to models developed within the artificial neural networks paradigm. The memory uses a standard table as its medium, where the information is stored in an indeterminate form, and the entropy plays a functional and operation role. The memory register operation abstracts the input cue with the current memory content and is productive; memory recognition is performed through a logical test; and memory retrieval is constructive. The three operations can be performed in parallel using very few computing resources. In our previous work we explored the auto-associative properties of the memory and performed experiments to store, recognize and retrieve manuscript digits and letters with complete and incomplete cues, and also to recognize and learn phones, with satisfactory results. In such experiments a designated memory register was used to store all the objects of the same class, whereas in the present study we remove such restriction and use a single memory register to store all the objects in the domain. In this novel setting we explore the production of emerging objects and relations, such that cues are used not only to retrieve remembered objects, but also related and imaged objects, and to produce association chains. The present model supports the view that memory and classification are independent functions both conceptually and architecturally. The memory system can store images of the different modalities of perception and action, possibly multimodal, and offers a novel perspective on the imagery debate and computational models of declarative memory.
VoiceCloak

Faced with the threat of identity leakage during voice data publishing, users are engaged in a privacy-utility dilemma when enjoying the utility of voice services. Existing machine-centric studies employ direct modification or text-based re-synthesis to de-identify users' voices but cause inconsistent audibility for human participants in emerging online communication scenarios, such as virtual meetings. In this paper, we propose a human-centric voice de-identification system, VoiceCloak, which uses adversarial examples to balance the privacy and utility of voice services. Instead of typical additive examples inducing perceivable distortions, we design a novel convolutional adversarial example that modulates perturbations into real-world room impulse responses. Benefiting from this, VoiceCloak could preserve user identity from exposure by Automatic Speaker Identification (ASI), while remaining the voice perceptual quality for non-intrusive de-identification. Moreover, VoiceCloak learns a compact speaker distribution through a conditional variational auto-encoder to synthesize diverse targets on demand. Guided by these pseudo targets, VoiceCloak constructs adversarial examples in an input-specific manner, enabling any-to-any identity transformation for robust de-identification. Experimental results show that VoiceCloak could achieve over 92% and 84% successful de-identification on mainstream ASIs and commercial systems with excellent voiceprint consistency, speech integrity, and audio quality.
Study on the Compressive Properties of an Elastomeric Porous Cylinder Using 360° Three-Dimensional Digital Image Correlation System

To study the compressive properties of an elastomeric porous cylinder, a 360° 3D digital image correlation (DIC) system is proposed. This compact vibration isolation table system captures different segments of the object from four different angles and fields of view, enabling a comprehensive measurement of the full surface of the object. To increase the stitching quality, a coarse-fine coordinate matching method is presented. First, a three-dimensional rigid body calibration auxiliary block is employed to track motion trajectory, which enables preliminary matching of four 3D DIC sub-systems. Subsequently, scattered speckle information characteristics guide fine matching. The accuracy of the 360° 3D DIC system is verified through a three-dimensional shape measurement conducted on a cylindrical shell, and the maximum relative error of the shell's diameter is 0.52%. A thorough investigation of the 3D compressive displacements and strains exerted on the full surface of an elastomeric porous cylinder are investigated. The results demonstrate the robustness of the proposed 360° measuring system on calculating images with voids and indicate a negative Poisson's ratio of periodically cylindrical porous structures.
Content Analysis of Emoji and Emoticon Use in Clinical Texting Systems

Emoji and emoticons are quickly becoming an omnipresent feature of virtual communication. As health care systems increasingly adopt clinical texting applications, it is critical to understand how clinicians use these ideograms with colleagues and how it may affect their interactions.To evaluate the functions that emoji and emoticons serve in clinical text messages.This qualitative study's content analysis of clinical text messages from a secure clinical messaging platform was conducted to assess the communicative function of emoji and emoticons. The analysis included messages sent by hospitalists to other health care clinicians. A subset of a random 1% sample of all message threads, which included at least 1 emoji or emoticon, on a clinical texting system used by a large, Midwestern US hospital from July 2020 until March 2021 were analyzed. A total of 80 hospitalists participated in the candidate threads.Whether and what kind of emoji or emoticon was deployed in each reviewed thread was tabulated by the study team. The communicative function of each emoji and emoticon was assessed according to a prespecified coding scheme.A total of 80 hospitalists (49 [61%] male; 30 [37%] Asian, 5 [6%] Black or African American, 2 [3%] Hispanic or Latinx, 42 [53%] White; of 41 with age data, 13 [32%] aged 25-34 years, 19 [46%] aged 35-44 years) participated in the 1319 candidate threads. Within the sample of 1319 threads, 7% of threads (155 unique messages) contained at least 1 emoji or emoticon. The majority (94 [61%]) functioned emotively, that is, conveyed the internal state of the sender, and 49 (32%) served to open, maintain, or close communication. No evidence was identified that they caused confusion or were seen as inappropriate.This qualitative study found that when clinicians use emoji and emoticons in secure clinical texting systems, these symbols function primarily to convey new and interactionally salient information. These results suggest that concerns about the professionalism of emoji and emoticon use may be unwarranted.
Grep: A Graph Learning Based Database Partitioning System

Database partitioning is a fundamental but challenging task in distributed databases, which selects specific columns as a partitioning key for each table and uses the partitioning key to allocate the table data into different compute nodes in order to maximize the performance. However, this problem is NP-hard and existing distributed databases require users to manually specify the partitioning keys, which may cause potential performance degradation. Although reinforcement learning based methods have been proposed, they have several limitations. First, they do not capture the complex data distributions and query access patterns, and thus involve high computation cost across different compute nodes to answer a query. Second, they involve an expensive step to repetitively partition the data into different compute nodes in order to train a learned key-selection model, which is a waste of time and resources. To address these limitations, we propose a practical learned database partitioning system Grep. We first adopt a graph model to encode data and query features, where vertices are columns, edges are query relations, and the weights of columns are computed based on the localized graph structures (e.g., data diversity, joined columns). We then utilize graph neural networks to embed the partitioning factors into embedding vectors in order to capture the data and query correlations. Next we propose a key-selection model to select appropriate partitioning keys based on the graph model. Finally, we propose an evaluation model to estimate the partitioning performance without actually partitioning the database. We have implemented Grep in a commercial distributed database, and experiments show the effectiveness of our system (e.g., 68% higher throughput for 30K queries in a real banking scenario).
How Challenging is Multimodal Irony Detection?

The possibility that social networks offer to attach audio, video, and images to textual information has led many users to create messages with multimodal irony. Over the last years, a series of approaches have emerged trying to leverage all these formats to address the problem of multimodal irony detection. The question that the present work tries to answer is whether multimodal irony systems are being properly evaluated. More specifically, this work studies the most popular dataset used in multimodal irony detection combining text and images, identifying whether image information is really necessary to understand the ironic intention of the text. This corpus was compared to a text-only corpus, and different textual and multimodal Transformer models were evaluated on them. This study reveals that, in many situations, Transformer models were able to identify the ironic nature of the posts considering only textual information.
A real-time algorithm for human action recognition in RGB and thermal video

Monitoring the movement and actions of humans in video in real-time is an important task. We present a deep learning based algorithm for human action recognition for both RGB and thermal cameras. It is able to detect and track humans and recognize four basic actions (standing, walking, running, lying) in real-time on a notebook with a NVIDIA GPU. For this, it combines state of the art components for object detection (Scaled-YoloV4), optical flow (RAFT) and pose estimation (EvoSkeleton). Qualitative experiments on a set of tunnel videos show that the proposed algorithm works robustly for both RGB and thermal video.
Application of Gaofen-5 hyperspectral data in uranium exploration: a case study of Weijing in Inner Mongolia, China

Chinese Gaofen-5 (GF-5) satellite is the world's first full-spectrum hyperspectral satellite to achieve comprehensive observations of the atmosphere and land. The Advanced Hyperspectral Imager (AHSI) carried by GF-5 can acquire 330- chanel imagery covering 390~2500 nm. However, the application of GF-5 AHSI imagery in uranium exploration is currently less. In this paper, the AHSI imagery was used for prospecting uranium mineralization in the Weijing, Inner Mongolia, China. The matched filter (MF) and threshold segmentation were used for mapping goethite, Al-high, Al- medium and Al-poor sericite. And the principal component analysis (PCA) and gray-level co-occurrence matrix (GLCM) were used to extract the texture information of the study area. Subsequently, combined with geological information, the relationship between alteration information, texture complexity and uranium mineralization was discussed, and it was pointed out that goethite, Al-medium, Al-poor , and Al-high sericite and texture complexity in this area can be used as indicators of uranium mineralization. Finally, two prospects were delineated, which will guide the follow-up uranium exploration in this area and promote the application of GF-5 AHSI data.
Synchronization-Aware NAS for an Efficient Collaborative Inference on Mobile Platforms

Previous neural architecture search (NAS) approaches for mobile platforms have achieved great success in designing a slim-but-accurate neural network that is generally well-matched to a single computing unit such as a CPU or GPU. However, as recent mobile devices consist of multiple heterogeneous computing units, the next main challenge is to maximize both accuracy and efficiency by fully utilizing multiple available resources. We propose an ensemble-like approach with intermediate feature aggregations, namely synchronizations, for active collaboration between individual models on a mobile device. A main challenge is to determine the optimal synchronization strategies for achieving both performance and efficiency. To this end, we propose SyncNAS to automate the exploration of synchronization strategies for collaborative neural architectures that maximize utilization of heterogeneous computing units on a target device. We introduce a novel search space for synchronization strategy and apply Monte Carlo tree search (MCTS) algorithm to improve the sampling efficiency and reduce the search cost. On ImageNet, our collaborative model based on MobileNetV2 achieves 2.7% top-1 accuracy improvement within the baseline latency budget. Under the reduced target latency down to half, our model maintains higher accuracy than its baseline model, owing to the enhanced utilization and collaboration. As an impact of MCTS, SyncNAS reduces its search cost by up to 21x in searching for the optimal strategy.
Generic Mechanism for Reducing Repetitions in Encoder-decoder Models

Encoder-decoder models have been commonly used; they have achieved state-of-the-art results for many natural language generation tasks. However, according to the reports of previous studies, encoder-decoder models suffer from generating redundant repetitions. Thus, we herein propose a repetition reduction module (RRM) for encoder-decoder models that estimates the semantic difference of a source sentence before and after it is fed into the model to capture the consistency between the two sides. As an autoencoder, the proposed mechanism supervises the training of encoder-decoder models to reduce the number of repeatedly generated tokens. The evaluation results of the publicly available machine translation and response generation datasets demonstrate the effectiveness of our proposal.
Instance-Aware Prompt Learning for Language Understanding and Generation

Prompt learning has emerged as a new paradigm for leveraging pre-trained language models (PLMs) and has shown promising results in downstream tasks with only a slight increase in parameters. However, the current usage of fixed prompts, whether discrete or continuous, assumes that all samples within a task share the same prompt. This assumption may not hold for tasks with diverse samples that require different prompt information. To address this issue, we propose an instance-aware prompt learning method that learns a different prompt for each instance. Specifically, we suppose that each learnable prompt token has a different contribution to different instances, and we learn the contribution by calculating the relevance score between an instance and each prompt token. The contribution-weighted prompt would be instance aware. We apply our method to both unidirectional and bidirectional PLMs on both language understanding and generation tasks. Extensive experiments demonstrate that our method achieves comparable results using as few as 1.5% of the parameters of PLMs tuned and obtains considerable improvements compared with strong baselines. In particular, our method achieves state-of-the-art results using ALBERT-xxlarge-v2 on the SuperGLUE few-shot learning benchmark. 1
Benchmarking Message Queues

Message queues are a way for different software components or applications to communicate with each other asynchronously by passing messages through a shared buffer. This allows a sender to send a message without needing to wait for an immediate response from the receiver, which can help to improve the system’s performance, reduce latency, and allow components to operate independently. In this paper, we compared and evaluated the performance of four popular message queues: Redis, ActiveMQ Artemis, RabbitMQ, and Apache Kafka. The aim of this study was to provide insights into the strengths and weaknesses of each technology and to help practitioners choose the most appropriate solution for their use case. We primarily evaluated each technology in terms of latency and throughput. Our experiments were conducted using a diverse array of workloads to test the message queues under various scenarios. This enables practitioners to evaluate the performance of the systems and choose the one that best meets their needs. The results show that each technology has its own pros and cons. Specifically, Redis performed the best in terms of latency, whereas Kafka significantly outperformed the other three technologies in terms of throughput. The optimal choice depends on the specific requirements of the use case. This paper presents valuable insights for practitioners and researchers working with message queues. Furthermore, the results of our experiments are provided in JSON format as a supplement to this paper.
Reconsidering Generic Composition: The Modes A10, A11 and A12 are Insecure

Authenticated Encryption ( $$\textsf{AE}$$ ) achieves privacy and authenticity with a single scheme. It is possible to obtain an $$\textsf{AE}$$ scheme gluing together an encryption scheme (privacy secure) and a Message Authentication Code (authenticity secure). This approach is called generic composition and its security has been studied by Namprempre et al. [20]. They looked into all the possible gluings of an encryption scheme with a secure $$\textsf{MAC}$$ to obtain a nonce-based $$\textsf{AE}$$ -scheme. The encryption scheme is either $$\textsf{IV}$$ -based (that is, with an additional random input, the initialization vector [ $$\textsf{IV}$$ ]) or nonce-based (with an input to be used once, the nonce). Nampremepre et al. assessed the security/insecurity of all possible composition combinations except for 4 (N4, A10, A11 and A12). Berti et al. [9] showed that N4 is insecure and that the remaining modes (A10, A11, and A12) are either all secure or insecure. Here, we prove that these modes are all insecure with a counterexample.
RegKey: A Register-based Implementation of ECC Signature Algorithms Against One-shot Memory Disclosure

To ensure the security of cryptographic algorithm implementations, several cryptographic key protection schemes have been proposed to prevent various memory disclosure attacks. Among them, the register-based solutions do not rely on special hardware features and offer better applicability. However, due to the size limitation of register resources, the performance of register-based solutions is much worse than conventional cryptosystem implementations without security enhancements. This paper presents RegKey, an efficient register-based implementation of ECC (elliptic curve cryptography) signature algorithms. Different from other schemes that protect the whole cryptographic operations, RegKey only uses CPU registers to execute simple but critical operations, significantly reducing the usage of register resources and performance overheads. To achieve this goal, RegKey splits the ECC signing into two parts, (1) complex elliptic curve group operations on non-sensitive data in main memory as normal implementations, and (2) simple prime field operations on sensitive data inside CPU registers. RegKey guarantees the plaintext private key and random number used for signing only appear in registers to effectively resist one-shot memory disclosure attacks such as cold-boot attacks and warm-boot attacks, which are usually launched by physically accessing the victim machine to acquire partial or even entire memory data but only once. Compared with existing cryptographic key protection schemes, the performance of RegKey is greatly improved. Regkey is applicable to different platforms because it does not rely on special CPU hardware features. Since RegKey focuses on one-shot memory disclosure instead of persistent software-based attacks, it works as a choice suitable for embedded devices or offline machines where physical attacks are the main threat.
A systematic literature review on Machine Learning Model evaluation on healthcare applications

Machine Learning (ML) models have been applied to solve problems in various fields, which necessarily involves proper evaluation of models to ensure performance. Once deployed, ML models are subject to performance issues, such as those related to changes in data (drift). This type of issue has prompted efforts in model analysis and maintenance, as well as in continual learning, which seeks the ability to continuously learn from a (continuous) stream of data. Therefore, it's important to understand and develop methodologies that can be used to evaluate ML models, making their use in real-world environments feasible. Amongst current areas of application for ML, one that stands out, in particular, is Machine Learning for Healthcare, especially in conjunction with Software for Decision Support of Medical Applications, which presents specific challenges for the evaluation and monitoring of models, particularly given that incorrect prediction or classification can lead to life-threatening situations. This paper presents a systematic literature review that aims at identifying state-of-the-art techniques for evaluating and maintaining ML models for healthcare in effective use in the real world.
Category-Intent Fusion Graph Neural Network for Session-based Recommendation

A user's interaction with an item is determined by a combination of intentions, such as a desire to purchase an electronic device while following a trend.However, these intentions are often unobservable, making it difficult to model user intentions and improve session recommendations.To tackle this problem, we propose a novel approach called the Category-Intent Graph Neural Network (CIGNN), which leverages the relationship between item categories and user intentions to provide accurate recommendations.We translate the category information into a compact representation, which represents the user's intent, and construct a category-intent fusion graph with item, category, and intent nodes.This graph connects multiple potential intents for each item in a session to capture user intentions and increase the expressiveness of item representations.The CIGNN model transfers information between intent, item, and category nodes, updating their representations alternately.Our experimental results on three benchmark datasets demonstrate the superiority of the CIGNN model over state-of-theart (SOTA) methods in session-based recommendation (SBR).
Detecting the relative phase between different frequency components of a photon using a three-level <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">Λ</mml:mi></mml:math> atom coupled to a waveguide

The authors study the scattering of a two-tone photon with a three-level $\mathrm{\ensuremath{\Lambda}}$ atom in a superposition of ground states, when the atom is coupled to a waveguide. The system can be used to detect both the relative phase between the photon frequencies and the relative phase of the atomic ground states as the output scattering depends on both quantities.