Reviewerly/exHarmony huggingface dataset:
Negative Samples are at Large: Leveraging Hard-Distance Elastic Loss for Re-identification

We present a Momentum Re-identification (MoReID) framework that can leverage a very large number of negative samples in training for general re-identification task. The design of this framework is inspired by Momentum Contrast (MoCo), which uses a dictionary to store current and past batches to build a large set of encoded samples. As we find it less effective to use past positive samples which may be highly inconsistent to the encoded feature property formed with the current positive samples, MoReID is designed to use only a large number of negative samples stored in the dictionary. However, if we train the model using the widely used Triplet loss that uses only one sample to represent a set of positive/negative samples, it is hard to effectively leverage the enlarged set of negative samples acquired by the MoReID framework. To maximize the advantage of using the scaled-up negative sample set, we newly introduce Hard-distance Elastic loss (HE loss), which is capable of using more than one hard sample to represent a large number of samples. Our experiments demonstrate that a large number of negative samples provided by MoReID framework can be utilized at full capacity only with the HE loss, achieving the state-of-the-art accuracy on three re-ID benchmarks, VeRi-776, Market-1501, and VeRi-Wild.
Semantic-Aware Fine-Grained Correspondence

Establishing visual correspondence across images is a challenging and essential task. Recently, an influx of self-supervised methods have been proposed to better learn representations for visual correspondence. However, we find that these methods often fail to leverage semantic information and over-rely on the matching of low-level features. In contrast, human vision is capable of distinguishing between distinct objects as a pretext to tracking. Inspired by this paradigm, we propose to learn semantic-aware fine-grained correspondence. Firstly, we demonstrate that semantic correspondence is implicitly available through a rich set of image-level self-supervised methods. We further design a pixel-level self-supervised learning objective which specifically targets fine-grained correspondence. For downstream tasks, we fuse these two kinds of complementary correspondence representations together, demonstrating that they boost performance synergistically. Our method surpasses previous state-of-the-art self-supervised methods using convolutional networks on a variety of visual correspondence tasks, including video object segmentation, human pose tracking, and human part tracking.
Bridging Formal Methods and Machine Learning with Global Optimisation

Formal methods and machine learning are two research fields with drastically different foundations and philosophies. Formal methods utilise mathematically rigorous techniques for the specification, development and verification of software and hardware systems. Machine learning focuses on pragmatic approaches to gradually improve a parameterised model by observing a training data set. While historically the two fields lack communication, this trend has changed in the past few years with an outburst of research interest in the robustness verification of neural networks. This paper will briefly review these works, and focus on the urgent need for broader, and more in-depth, communication between the two fields, with the ultimate goal of developing learning-enabled systems with not only excellent performance but also acceptable safety and security. We present a specification language, MLS $$^2$$ , and show that it can express a set of known safety and security properties, including generalisation, uncertainty, robustness, data poisoning, backdoor, model stealing, membership inference, model inversion, interpretability, and fairness. To verify MLS $$^2$$ properties, we promote the global optimisation based methods, which have provable guarantees on the convergence to the optimal solution. Many of them have theoretical bounds on the gap between current solutions and the optimal solution.
Secret Can Be Public: Low-Memory AEAD Mode for High-Order Masking

We propose a new AEAD mode of operation for an efficient countermeasure against side-channel attacks. Our mode achieves the smallest memory with high-order masking, by minimizing the states that are duplicated in masking. An s-bit key-dependent state is necessary for achieving s-bit security, and the conventional schemes always protect the entire s bits with masking. We reduce the protected state size by introducing an unprotected state in the key-dependent state: we protect only a half and give another half to a side-channel adversary. Ensuring independence between the unprotected and protected states is the key technical challenge since mixing these states reveals the protected state to the adversary. We propose a new mode $$\textsf{HOMA}$$ that achieves s-bit security using a tweakable block cipher with the s/2-bit block size. We also propose a new primitive for instantiating $$\textsf{HOMA}$$ with $$s=128$$ by extending the SKINNY tweakable block cipher to a 64-bit plaintext block, a 128-bit key, and a $$(256+3)$$ -bit tweak. We make hardware performance evaluation by implementing $$\textsf{HOMA}$$ with high-order masking for $$d \le 5$$ . For any $$d > 0$$ , $$\textsf{HOMA}$$ outperforms the current state-of-the-art $$\textsf{PFB}\_\textsf{Plus}$$ by reducing the circuit area larger than that of the entire S-box.
Performance analysis of various image steganography techniques

Information hiding is the most interesting and prominent field of data security. With the evolution of computational infrastructure, covert communication techniques have received an inclusive acknowledgment among researchers as well as common participants of data communication. Among all, an image steganography technique which is a field of data hiding is one of the foremost choices among experts. The foremost defiance in scheming a steganographic system is to preserve an adequate equilibrium among the measures. The objective of this study is to present a all-inclusive survey of disparate existing images steganography techniques (ISTs) with regard to different performance assessment standards, such as (1) camouflage image (CI) quality, (2) capacity, and (3) robustness to different attacks. Further, the underlying challenges and future directions are also highlighted.
DISCO

Today, software developers work on complex and fast-moving projects that often require instant assistance from other domain and subject matter experts. Chat servers such as Discord facilitate live communication and collaboration among developers all over the world. With numerous topics discussed in parallel, mining and analyzing the chat data of these platforms would offer researchers and tool makers opportunities to develop software tools and services such as automated virtual assistants, chat bots, chat summarization techniques, Q&A thesaurus, and more.
Zero-Shot Temporal Action Detection via Vision-Language Prompting

Existing temporal action detection (TAD) methods rely on large training data including segment-level annotations, limited to recognizing previously seen classes alone during inference. Collecting and annotating a large training set for each class of interest is costly and hence unscalable. Zero-shot TAD (ZS-TAD) resolves this obstacle by enabling a pre-trained model to recognize any unseen action classes. Meanwhile, ZS-TAD is also much more challenging with significantly less investigation. Inspired by the success of zero-shot image classification aided by vision-language (ViL) models such as CLIP, we aim to tackle the more complex TAD task. An intuitive method is to integrate an off-the-shelf proposal detector with CLIP style classification. However, due to the sequential localization (e.g., proposal generation) and classification design, it is prone to localization error propagation. To overcome this problem, in this paper we propose a novel zero- $$\underline{S}$$ hot $$\underline{T}$$ emporal $$\underline{A}$$ ction detection model via Vision- $$\underline{L}$$ anguag $$\underline{E}$$ prompting (STALE). Such a novel design effectively eliminates the dependence between localization and classification by breaking the route for error propagation in-between. We further introduce an interaction mechanism between classification and localization for improved optimization. Extensive experiments on standard ZS-TAD video benchmarks show that our STALE significantly outperforms state-of-the-art alternatives. Besides, our model also yields superior results on supervised TAD over recent strong competitors. The PyTorch implementation of STALE is available on https://github.com/sauradip/STALE .
Prostate cancer malignancy detection and localization from mpMRI using auto-deep learning as one step closer to clinical utilization

Automatic diagnosis of malignant prostate cancer patients from mpMRI has been studied heavily in the past years. Model interpretation and domain drift have been the main road blocks for clinical utilization. As an extension from our previous work we trained on a public cohort with 201 patients and the cropped 2.5D slices of the prostate glands were used as the input, and the optimal model were searched in the model space using autoKeras. As an innovative move, peripheral zone (PZ) and central gland (CG) were trained and tested separately, the PZ detector and CG detector were demonstrated effective in highlighting the most suspicious slices out of a sequence, hopefully to greatly ease the workload for the physicians.
Algorithm Recommendation and Performance Prediction Using Meta-Learning

In the last years, the number of machine learning algorithms and their parameters has increased significantly. On the one hand, this increases the chances of finding better models. On the other hand, it increases the complexity of the task of training a model, as the search space expands significantly. As the size of datasets also grows, traditional approaches based on extensive search start to become prohibitively expensive in terms of computational resources and time, especially in data streaming scenarios. This paper describes an approach based on meta-learning that tackles two main challenges. The first is to predict key performance indicators of machine learning models. The second is to recommend the best algorithm/configuration for training a model for a given machine learning problem. When compared to a state-of-the-art method (AutoML), the proposed approach is up to 130x faster and only 4% worse in terms of average model quality. Hence, it is especially suited for scenarios in which models need to be updated regularly, such as in streaming scenarios with big data, in which some accuracy can be traded for a much shorter model training time.
Clustering: an R library to facilitate the analysis and comparison of cluster algorithms

Abstract Clustering is an unsupervised learning method that divides data into groups of similar features. Researchers use this technique to categorise and automatically classify unlabelled data to reveal data concentrations. Although there are other implementations of clustering algorithms in R, this paper introduces the Clustering library for R, aimed at facilitating the analysis and comparison between clustering algorithms. Specifically, the library uses relevant clustering algorithms from the literature with two objectives: firstly to group data homogeneously by establishing differences between clusters and secondly to generate a ranking between the algorithms and the attributes of a data set to obtain the optimal number of clusters. Finally, it is crucial to highlight the added value that the library provides through its interactive graphical user interface, where experiments can be easily configured and executed without requiring expert knowledge of the parameters of each algorithm.
Repairing Failure-inducing Inputs with Input Reflection

Trained with a sufficiently large training and testing dataset, Deep Neural Networks (DNNs) are expected to generalize. However, inputs may deviate from the training dataset distribution in real deployments. This is a fundamental issue with using a finite dataset, which may lead deployed DNNs to mis-predict in production.
Why Are Proofs Relevant in Proof-Relevant Models?

Relational models of λ-calculus can be presented as type systems, the relational interpretation of a λ-term being given by the set of its typings. Within a distributors-induced bicategorical semantics generalizing the relational one, we identify the class of ‘categorified’ graph models and show that they can be presented as type systems as well. We prove that all the models living in this class satisfy an Approximation Theorem stating that the interpretation of a program corresponds to the filtered colimit of the denotations of its approximants. As in the relational case, the quantitative nature of our models allows to prove this property via a simple induction, rather than using impredicative techniques. Unlike relational models, our 2-dimensional graph models are also proof-relevant in the sense that the interpretation of a λ-term does not contain only its typings, but the whole type derivations. The additional information carried by a type derivation permits to reconstruct an approximant having the same type in the same environment. From this, we obtain the characterization of the theory induced by the categorified graph models as a simple corollary of the Approximation Theorem: two λ-terms have isomorphic interpretations exactly when their B'ohm trees coincide.
A High-Level Separation Logic for Heap Space under Garbage Collection

We present a Separation Logic with space credits for reasoning about heap space in a sequential call-by-value lambda-calculus equipped with garbage collection and mutable state. A key challenge is to design sound, modular, lightweight mechanisms for establishing the unreachability of a block. Prior work in this area uses pointed-by assertions to keep track of the predecessors of every block, but is carried out in the setting of an assembly-like programming language. We take up the challenge in the setting of a high-level language, where a key problem is to identify and reason about the memory locations that the garbage collector considers as roots. For this purpose, we propose novel "stackable" assertions, which keep track of the existence of stack-to-heap pointers without explicitly recording their origin. Furthermore, we explain how to reason about closures -- concrete heap-allocated data structures that implement the abstract concept of a first-class function. We demonstrate the expressiveness and tractability of our program logic via a range of examples, including recursive functions on linked lists, objects implemented using closures and mutable internal state, recursive functions in continuation-passing style, and three stack implementations that exhibit different space bounds. These last three examples illustrate reasoning about the reachability of the items stored in a container as well as amortized reasoning about space. All of our results are proved in Coq on top of Iris.
SHE: A Generic Framework for Data Stream Mining over Sliding Windows

1Data stream mining over a sliding window is a fundamental problem in many applications, such as financial data trackers, intrusion detection and QoS. To meet the demand for high throughput of high speed data streams, sliding window algorithms turn to hardware platforms including FPGA/ASIC and programmable switches. These hardware platforms have three constraints for algorithms running on, which are 1) small memory usage 2) single stage memory access and 3) limited concurrent memory access. Algorithms perfectly fit in with these constraints will enable a highest utilization of these hardware platforms. However, no existing sliding window algorithm is specifically designed for hardware platforms. In this paper, we propose the Sliding Hardware Estimator (SHE), which is a generic framework that extends existing fixed window algorithms to sliding windows on hardware platforms. The key idea of SHE is that, during insertions we approximately delete out-dated information with little time and space overhead, while during queries we design sophisticated techniques to minimize error. We have fully implemented our SHE on FPGA, achieving a throughput of 544 Mips. We apply SHE to four typical data stream mining tasks. Experimental results show that, when compared with the state-of-the-art which cannot be implemented in hardware, SHE reduces the error by up to 100 times in membership queries. All related source codes are released at Github.
Influence of the state reform of primary education on the professional training of future teachers

The purpose of the study was the analysis of the implementation of the reform of primary education as a structural model of the formation of the preparation of the future elementary school, for the design of the educational environment. The research methodology used included the analysis of legislative documentation to determine the requirements for the professional activity of an elementary school teacher. The procedure and features of actions for the development of a structural-functional model, of the formation of the readiness of future elementary school teachers to design an educational environment include: analysis of objects; choice of the form of the model; theoretical, methodical, spatio-temporal, material-technical, legal substantiation; the choice of a factor of formation of the system; establishment of connections and dependencies of the main components. The content of the developed main blocks of the model: meta - purpose and task; methodological - approaches and principles; content-procedure - updated content of the discipline content of the vocational and practical training cycle, forms of organization of the educational process, methods, means of training and diagnostics; organizational and pedagogical conditions; stages: adaptation, intensification and identification. It is concluded that the developed model reflects the actual educational process of achieving the expected results.
Subjective and objective quality assessment for volumetric video

Volumetric video (VV) is a novel form of video that allows recreation of real-world scenes in 3D with users consuming the content from any viewpoint they desire. This makes VV best suited for augmented reality (AR) or virtual reality (VR) applications. This freedom necessitates increased user interaction with the VV itself, which brings new challenges to its visual quality assessment. In this chapter, various aspects of VV quality assessment using subjective user studies and objective quality estimation methods are discussed. These aspects include the manner of representing 3D models, mode of interaction, display settings (e.g., whether AR or VR headsets are used), rendering parameters, and how the characteristics of point clouds or meshes are used in quality estimation. The chapter discusses the advantages and disadvantages of different methods and provides take away messages for researchers.
Weightless Neural Networks for Efficient Edge Inference

Weightless neural networks (WNNs) are a class of machine learning model which use table lookups to perform inference, rather than the multiply-accumulate operations typical of deep neural networks (DNNs). Individual weightless neurons are capable of learning non-linear functions of their inputs, a theoretical advantage over the linear neurons in DNNs, yet state-of-the-art WNN architectures still lag behind DNNs in accuracy on common classification tasks. Additionally, many existing WNN architectures suffer from high memory requirements, hindering implementation. In this paper, we propose a novel WNN architecture, BTHOWeN, with key algorithmic and architectural improvements over prior work, namely counting Bloom filters, hardware-friendly hashing, and Gaussian-based nonlinear thermometer encodings. These enhancements improve model accuracy while reducing size and energy per inference. BTHOWeN targets the large and growing edge computing sector by providing superior latency and energy efficiency to both prior WNNs and comparable quantized DNNs. Compared to state-of-the-art WNNs across nine classification datasets, BTHOWeN on average reduces error by more than 40% and model size by more than 50%. We demonstrate the viability of a hardware implementation of BTHOWeN by presenting an FPGA-based inference accelerator, and compare its latency and resource usage against similarly accurate quantized DNN inference accelerators, including multi-layer perceptron (MLP) and convolutional models. The proposed BTHOWeN models consume almost 80% less energy than the MLP models, with nearly 85% reduction in latency. In our quest for efficient ML on the edge, WNNs are clearly deserving of additional attention.
Facebook and drug driving: Does online sharing work against road safety countermeasures?

Introduction: Enhancements to technology have transformed several aspects of road safety, communication, and connectivity. At the intersection of these, scholars have begun to speculate whether certain aspects of technology may provide motorists ways to engage in illegal and dangerous driving behaviors without consequences. Police traffic operations such as Roadside Drug Testing are intended to occur “anywhere, anytime” to communicate to motorists that they should not risk offending. A potential challenge for road safety is the emergence of Facebook police location pages and groups, where users can share the locations of police operations. Method: In this study, the authors examined two Facebook police location groups and three pages from Queensland (Australia) and conducted a content analysis of posts related to Roadside Drug Testing operations and a thematic analysis of comments on these posts. A total of 282 posts related to Roadside Drug Testing were identified between February and April 2021, with 1,823 comments made. Results: The findings suggest that some users: (a) had lived experiences of avoiding punishment for drug driving; (b) remain unaware of how long to wait between drug consumption and driving; (c) perceived Roadside Drug Testing operations to be for the purpose of revenue raising; and (d) changed their driving behavior upon seeing an operation. Conclusions: These findings suggest consideration is needed regarding the responsibility of both Facebook, and the government, in allowing groups and pages to exist that undermine law enforcement. Practical Applications: Regarding practice, the comments suggest that greater education is needed regarding “safe” times to drive after drug taking.