Reviewerly/exHarmony huggingface dataset:
Public Key Encryption with Secure Key Leasing

We introduce the notion of public key encryption with secure key leasing (PKE-SKL). Our notion supports the leasing of decryption keys so that a leased key achieves the decryption functionality but comes with the guarantee that if the quantum decryption key returned by a user passes a validity test, then the user has lost the ability to decrypt. Our notion is similar in spirit to the notion of secure software leasing (SSL) introduced by Ananth and La Placa (Eurocrypt 2021) but captures significantly more general adversarial strategies. (In more detail, our adversary is not restricted to use an honest evaluation algorithm to run pirated software.) Our results can be summarized as follows: Prior to our work, the notion of single decryptor encryption (SDE) has been studied in the context of PKE (Georgiou and Zhandry, Eprint 2020) and FE (Kitigawa and Nishimaki, Asiacrypt 2022) but all their constructions rely on strong assumptions including indistinguishability obfuscation. In contrast, our constructions do not require any additional assumptions, showing that PKE/IBE/ABE/FE can be upgraded to support secure key leasing for free.
Tangible Immersive Trauma Simulation: Is Mixed Reality the next level of medical skills training?

In medical simulation training two approaches are currently rather disjunct: realistic manikins are used to teach physical skills and procedures and VR systems are used to train situation assessment and decision making. We propose a mixed reality approach, which allows trainees to use real tools and their hands when interacting with a physical manikin overlaid with a responsive virtual avatar. In close exchange with first responder organizations, we developed and evaluated an MR training scenario. In the scenario, users can talk to injured people in a car accident, assess the threat of the environment, and utilize real medical equipment. Participants experienced high levels of physical- and self-presence, increased stress levels, and reported a high technology acceptance. The proposed main requirements of first responders regarding haptic multi-sensory skill training in MR and the lessons learned from the workshop aim to guide the design of training solutions for medical training in MR.
Rapsai: Accelerating Machine Learning Prototyping of Multimedia Applications through Visual Programming

In recent years, there has been a proliferation of multimedia applications that leverage machine learning (ML) for interactive experiences. Prototyping ML-based applications is, however, still challenging, given complex workflows that are not ideal for design and experimentation. To better understand these challenges, we conducted a formative study with seven ML practitioners to gather insights about common ML evaluation workflows.
Block-Hash Signature (BHS) for Transaction Validation in Smart Contracts for Security and Privacy using Blockchain

Some of the well-known signature techniques like Winternitz and Lamport are not considered to be very appropriate for the usage of hashing or smart contracts in Blockchains security because of their size O(n2), which is prominently too high. Although in Blockchain, the security concern is on the top priority because of its distributed P2P design still, the security enhancement is required to sign and verify the documents forwarded to the peers, especially in Hyperledger Fabric. Here, this paper presents a new signature technique “Block-Hash” to enhance Blockchain security by using it in smart contracts as well as hashing with size 3Xn bits (n=256, generally for SHA-256 Hashing) and which can score 112 bits security. The proposed signature can be used appropriately for signing a smart contract by the endorser or committer node. Also, it can be used with a hash algorithm in forming a Merkle tree. Apart from the description and implementation of Block-Hash Signature, this paper has covered the analysis of its security and correctness measures with a table for result comparison.
Spatial Context-Aware Object-Attentional Network for Multi-Label Image Classification

Multi-label image classification is a fundamental but challenging task in computer vision. To tackle the problem, the label-related semantic information is often exploited, but the background context and spatial semantic information of related objects are not fully utilized. To address these issues, a multi-branch deep neural network is proposed in this paper. The first branch is designed to extract the discriminant information from regions of interest to detect target objects. In the second branch, a spatial context-aware approach is proposed to better capture the contextual information of an object in its surroundings by using an adaptive patch expansion mechanism. It helps the detection of small objects that are easily lost without the support of context information. The third one, the object-attentional branch, exploits the spatial semantic relations between the target object and its related objects, to better detect partially occluded, small or dim objects with the support of those easily detectable objects. To better encode such relations, an attention mechanism jointly considering the spatial and semantic relations between objects is developed. Two widely used benchmark datasets for multi-labeling classification, MS COCO and PASCAL VOC, are used to evaluate the proposed framework. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods for multi-label image classification.
Privacy-preserving deep learning on big data in cloud

In the analysis of big data, deep learning is a crucial technique. Big data analysis tasks are typically carried out on the cloud since it offers strong computer capabilities and storage areas. Nevertheless, there is a contradiction between the open nature of the cloud and the demand that data owners maintain their privacy. To use cloud resources for privacy-preserving data training, a viable method must be found. A privacy-preserving deep learning model (PPDLM) is suggested in this research to address this preserving issue. To preserve data privacy, we first encrypted the data using homomorphic encryption (HE) approach. Moreover, the deep learning algorithm's activation function—the sigmoid function—uses the least-squares method to process nonaddition and non-multiplication operations that are not allowed by homomorphic. Finally, experimental results show that PPDLM has a significant effect on the protection of data privacy information. Compared with Non-Privacy Preserving Deep Learning Model (NPPDLM), PPDLM has higher computational efficiency.
A Framework for Solving Explicit Arithmetic Word Problems and Proving Plane Geometry Theorems

This paper presents a framework for solving math problems stated in a natural language (NL) and applies the framework to develop algorithms for solving explicit arithmetic word problems and proving plane geometry theorems. We focus on problem understanding, that is, the transformation of a NL description of a math problem to a formal representation. We view this as a relation extraction problem, and adopt a greedy algorithm to extract the mathematical relations using a syntax-semantics model, which is a set of patterns describing how a syntactic pattern is mapped to its formal semantics. Our method yields a human readable solution that shows how the mathematical relations are extracted one at a time. We apply our framework to solve arithmetic word problems and prove plane geometry theorems. For arithmetic word problems, the extracted relations are transformed into a system of equations, and the equations are then solved to produce the solution. For plane geometry theorems, these extracted relations are input to an inference system to generate the proof. We evaluate our approach on a set of arithmetic word problems stated in Chinese, and two sets of plane geometry theorems stated in Chinese and English. Our algorithms achieve high accuracies on these datasets and they also show some desirable properties such as brevity of algorithm description and legibility of algorithm actions.
Experimentally induced limb-disownership in mixed reality

The seemingly stable construct of our bodily self depends on the continued, successful integration of multisensory feedback about our body, rather than its purely physical composition. Accordingly, pathological disruption of such neural processing is linked to striking alterations of the bodily self, ranging from limb misidentification to disownership, and even the desire to amputate a healthy limb. While previous embodiment research has relied on experimental setups using supernumerary limbs in variants of the Rubber Hand Illusion, we here used Mixed Reality to directly manipulate the feeling of ownership for one's own, biological limb. Using a Head-Mounted Display, participants received visual feedback about their own arm, from an embodied first-person perspective. In a series of three studies, in independent cohorts, we altered embodiment by providing visuotactile feedback that could be synchronous (control condition) or asynchronous (400 ms delay, Real Hand Illusion). During the illusion, participants reported a significant decrease in ownership of their own limb, along with a lowered sense of agency. Supporting the right-parietal body network, we found an increased illusion strength for the left upper limb as well as a modulation of the feeling of ownership during anodal transcranial direct current stimulation. Extending previous research, these findings demonstrate that a controlled, visuotactile conflict about one's own limb can be used to directly and systematically modulate ownership – without a proxy. This not only corroborates the malleability of body representation but questions its permanence. These findings warrant further exploration of combined VR and neuromodulation therapies for disorders of the bodily self.
A comparison of software for the evaluation of complex DNA profiles

We compare two open-source programs for the evaluation of evidential weight arising from complex DNA profiles recovered in a crime investigation. Here, “complex” means one or more of: low-template, degraded and mixed-source. Although software for complex DNA profile analysis has made great strides in recent years, the ability of courts to effectively scrutinise and challenge the reliability of the resulting evidence remains problematic. One key step is to compare different software on the same evidence, but there are currently few published comparisons in part because of the problems posed by restricted access to commercial software. We present here an extensive comparison between two open-source software, LikeLTD and EuroForMix. We find that despite different modelling assumptions the two programs generate similar results. The differences that we do identify can inform future improvements and can provide a benchmark for acceptable discrepancies between alternative programs.
Leveraging Label-Specific Discriminant Mapping Features for Multi-Label Learning

As an important machine learning task, multi-label learning deals with the problem where each sample instance (feature vector) is associated with multiple labels simultaneously. Most existing approaches focus on manipulating the label space, such as exploiting correlations between labels and reducing label space dimension, with identical feature space in the process of classification. One potential drawback of this traditional strategy is that each label might have its own specific characteristics and using identical features for all label cannot lead to optimized performance. In this article, we propose an effective algorithm named LSDM, i.e., leveraging label-specific discriminant mapping features for multi-label learning , to overcome the drawback. LSDM sets diverse ratio parameter values to conduct cluster analysis on the positive and negative instances of identical label. It reconstructs label-specific feature space which includes distance information and spatial topology information. Our experimental results show that combining these two parts of information in the new feature representation can better exploit the clustering results in the learning process. Due to the problem of diverse combinations for identical label, we employ simplified linear discriminant analysis to efficiently excavate optimal one for each label and perform classification by querying the corresponding results. Comparison with the state-of-the-art algorithms on a total of 20 benchmark datasets clearly manifests the competitiveness of LSDM.
A Survey on 3D Ultrasound Reconstruction Techniques

This book chapter aims to discuss the 3D ultrasound reconstruction and visualization. First, the various types of 3D ultrasound system are reviewed, such as mechanical, 2D array, position tracking-based freehand, and untracked-based freehand. Second, the 3D ultrasound reconstruction technique or pipeline used by the current existing system, which includes the data acquisition, data preprocessing, reconstruction method and 3D visualization, is discussed. The reconstruction method and 3D visualization will be emphasized. The reconstruction method includes the pixel-based method, volume-based method, and function-based method, accompanied with their benefits and drawbacks. In the 3D visualization, methods such as multiplanar reformatting, volume rendering, and surface rendering are presented. Lastly, its application in the medical field is reviewed as well.
Fixed That for You: Generating Contrastive Claims with Semantic Edits

Christopher Hidey, Kathy McKeown. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
A new optical density granulometry-based descriptor for the classification of prostate histological images using shallow and deep Gaussian processes

Prostate cancer is one of the most common male tumors. The increasing use of whole slide digital scanners has led to an enormous interest in the application of machine learning techniques to histopathological image classification. Here we introduce a novel family of morphological descriptors which, extracted in the appropriate image space and combined with shallow and deep Gaussian process based classifiers, improves early prostate cancer diagnosis. We decompose the acquired RGB image in its RGB and optical density hematoxylin and eosin components. Then, we define two novel granulometry-based descriptors which work in both, RGB and optical density, spaces but perform better when used on the latter. In this space they clearly encapsulate knowledge used by pathologists to identify cancer lesions. The obtained features become the inputs to shallow and deep Gaussian process classifiers which achieve an accurate prediction of cancer. We have used a real and unique dataset. The dataset is composed of 60 Whole Slide Images. For a five fold cross validation, shallow and deep Gaussian Processes obtain area under ROC curve values higher than 0.98. They outperform current state of the art patch based shallow classifiers and are very competitive to the best performing deep learning method. Models were also compared on 17 Whole Slide test Images using the FROC curve. With the cost of one false positive, the best performing method, the one layer Gaussian process, identifies 83.87% (sensitivity) of all annotated cancer in the Whole Slide Image. This result corroborates the quality of the extracted features, no more than a layer is needed to achieve excellent generalization results. Two new descriptors to extract morphological features from histological images have been proposed. They collect very relevant information for cancer detection. From these descriptors, shallow and deep Gaussian Processes are capable of extracting the complex structure of prostate histological images. The new space/descriptor/classifier paradigm outperforms state-of-art shallow classifiers. Furthermore, despite being much simpler, it is competitive to state-of-art CNN architectures both on the proposed SICAPv1 database and on an external database.
Shot-Net: A Convolutional Neural Network for Classifying Different Cricket Shots

Artificial Intelligence has become the new powerhouse of data analytics in this technological era. With advent of different Machine Learning and Computer Vision algorithms, applying them in data analytics has become a common trend. However, applying Deep Neural Networks in different sport data analyzing tasks and study the performance of these models is yet to be explored. Hence, in this paper, we have proposed a 13 layered Convolutional Neural Network referred as “Shot-Net” in order to classifying six categories of cricket shots, namely Cut Shot, Cover Drive, Straight Drive, Pull Shot, Scoop Shot and Leg Glance Shot. Our proposed model has achieved fairly high accuracy with low cross-entropy rate.
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU

High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address some of these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based on sparse linear algebra, which allow graph algorithms to be expressed in a performant, succinct, composable, and portable manner. In this paper, we examine the performance challenges of a linear-algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity , which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in “GraphBLAST”, the first high-performance linear algebra-based graph framework on NVIDIA GPUs that is open-source. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.
Neural Network based Continuous Conditional Random Field for Fine-grained Crime Prediction

Crime prediction has always been a crucial issue for public safety, and recent works have shown the effectiveness of taking spatial correlation, such as region similarity or interaction, for fine-grained crime modeling. In our work, we seek to reveal the relationship across regions for crime prediction using Continuous Conditional Random Field (CCRF). However, conventional CCRF would become impractical when facing a dense graph considering all relationship between regions. To deal with it, in this paper, we propose a Neural Network based CCRF (NN-CCRF) model that formulates CCRF into an end-to-end neural network framework, which could reduce the complexity in model training and improve the overall performance. We integrate CCRF with NN by introducing a Long Short-Term Memory (LSTM) component to learn the non-linear mapping from inputs to outputs of each region, and a modified Stacked Denoising AutoEncoder (SDAE) component for pairwise interactions modeling between regions. Experiments conducted on two different real-world datasets demonstrate the superiority of our proposed model over the state-of-the-art methods.
Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP

The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a “lucky” sub-network initialization being present rather than by helping the optimization process (Frankle& Carbin, 2019). Intriguingly, this phenomenon suggests that initialization strategies for DNNs can be improved substantially, but the lottery ticket hypothesis has only previously been tested in the context of supervised learning for natural image tasks. Here, we evaluate whether “winning ticket” initializations exist in two different domains: natural language processing (NLP) and reinforcement learning (RL).For NLP, we examined both recurrent LSTM models and large-scale Transformer models (Vaswani et al., 2017). For RL, we analyzed a number of discrete-action space tasks, including both classic control and pixel control. Consistent with workin supervised image classification, we confirm that winning ticket initializations generally outperform parameter-matched random initializations, even at extreme pruning rates for both NLP and RL. Notably, we are able to find winning ticket initializations for Transformers which enable models one-third the size to achieve nearly equivalent performance. Together, these results suggest that the lottery ticket hypothesis is not restricted to supervised learning of natural images, but rather represents a broader phenomenon in DNNs.
Intensity-Free Learning of Temporal Point Processes

Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating the conditional intensity function. However, parameterizing the intensity function usually incurs several trade-offs. We show how to overcome the limitations of intensity-based approaches by directly modeling the conditional distribution of inter-event times. We draw on the literature on normalizing flows to design models that are flexible and efficient. We additionally propose a simple mixture model that matches the flexibility of flow-based models, but also permits sampling and computing moments in closed form. The proposed models achieve state-of-the-art performance in standard prediction tasks and are suitable for novel applications, such as learning sequence embeddings and imputing missing data.
An Exploratory Analysis on Visual Counterfeits Using Conv-LSTM Hybrid Architecture

In recent years, with the advancements in the Deep Learning realm, it has been easy to create and generate synthetically the face swaps from GANs and other tools, which are very realistic, leaving few traces which are unclassifiable by human eyes. These are known as `DeepFakes' and most of them are anchored in video formats. Such realistic fake videos and images are used to create a ruckus and affect the quality of public discourse on sensitive issues; defaming one's profile, political distress, blackmailing and many more fake cyber terrorisms are envisioned. This work proposes a microscopic-typo comparison of video frames. This temporal-detection pipeline compares very minute visual traces on the faces of real and fake frames using Convolutional Neural Network (CNN) and stores the abnormal features for training. A total of 512 facial landmarks were extracted and compared. Parameters such as eye-blinking lip-synch; eyebrows movement, and position, are few main deciding factors that classify into real or counterfeit visual data. The Recurrent Neural Network (RNN) pipeline learns based on these features-fed inputs and then evaluates the visual data. The model was trained with the network of videos consisting of their real and fake, collected from multiple websites. The proposed algorithm and designed network set a new benchmark for detecting the visual counterfeits and show how this system can achieve competitive results on any fake generated video or image.