Reviewerly/exHarmony huggingface dataset:
Existing Privacy Protection Solutions

In this chapter, we outline the major developments of modern privacy study based on the survey work we have conducted [1–4]. Mainstream privacy protection techniques including anonymity, clustering-based, differential privacy, cryptography, and machine learning methods will be presented in the following sections.
Implementation of a quantum algorithm to estimate the energy of a particle in a finite square well potential on IBM quantum computer

In this paper, we implement a quantum algorithm—on IBM quantum devices, IBM QASM simulator and PPRC computer cluster—to find the energy values of the ground state and the first excited state of a particle in a finite square-well potential. We use the quantum phase estimation technique and the iterative one to execute the program on PPRC cluster and IBM devices, respectively. Our results obtained from executing the quantum circuits on the IBM classical devices show that our circuits succeed at simulating the system. However, duo to scattered results, we execute only the iterative phase estimation part of the circuit on the 5 qubit quantum devices to reduce the circuit size and obtain low-scattered results.
Unification of Edge Energy Grids for Empowering Small Energy Producers

The current energy landscape is largely comprised of big stakeholders, who are often the monopolistic drivers of their local market. This fact does not leave any room for smaller players to participate in this procedure by contributing their part in the energy pool. Moreover, the dynamic demand for power along with the current power production rate are not corelated, rendering the power distribution grid, a best effort network, prone to power failures, due to the inevitable irregularities in demand. This paper introduces a novel concept that allows small energy producers, such as solar panel grids, to offer their production excess through an intelligent energy brokerage blockchain-based framework. The proposed framework ingests the vast amounts of bigdata stemming from the distributed smart energy grids smart metering and allows for automatic commercial transactions of power between the participants of a dedicated marketplace. Values dynamically fluctuate depending on the real-time offer and demand and the grid’s state. Thus, all partaking stakeholders are able to take the most out of their product by leveraging the intelligence provided by the energy marketplace, and contribute to the overall stabilization of the energy grid.
Syntactic Perturbations Reveal Representational Correlates of Hierarchical Phrase Structure in Pretrained Language Models

While vector-based language representations from pretrained language models have set a new standard for many NLP tasks, there is not yet a complete accounting of their inner workings.In particular, it is not entirely clear what aspects of sentence-level syntax are captured by these representations, nor how (if at all) they are built along the stacked layers of the network.In this paper, we aim to address such questions with a general class of interventional, input perturbation-based analyses of representations from pretrained language models.Importing from computational and cognitive neuroscience the notion of representational invariance, we perform a series of probes designed to test the sensitivity of these representations to several kinds of structure in sentences.Each probe involves swapping words in a sentence and comparing the representations from perturbed sentences against the original.We experiment with three different perturbations: (1) random permutations of n-grams of varying width, to test the scale at which a representation is sensitive to word position; (2) swapping of two spans which do or do not form a syntactic phrase, to test sensitivity to global phrase structure; and (3) swapping of two adjacent words which do or do not break apart a syntactic phrase, to test sensitivity to local phrase structure.Results from these probes collectively suggest that Transformers build sensitivity to larger parts of the sentence along their layers, and that hierarchical phrase structure plays a role in this process.More broadly, our results also indicate that structured input perturbations widens the scope of analyses that can be performed on often-opaque deep learning systems, and can serve as a complement to existing tools (such as supervised linear probes) for interpreting complex black-box models. 1
Toward a Unified Metadata Schema for Ecological Momentary Assessment with Voice-First Virtual Assistants

Ecological momentary assessment (EMA) is used to evaluate subjects’ behaviors and moods in their natural environments, yet collecting real-time and self-report data with EMA is challenging due to user burden. Integrating voice into EMA data collection platforms through today’s intelligent virtual assistants (IVAs) is promising due to hands-free and eye-free nature. However, efficiently managing conversations and EMAs is non-trivial and time consuming due to the ambiguity of the voice input. We approach this problem by rethinking the data modeling of EMA questions and what is needed to deploy them on voice-first user interfaces. We propose a unified metadata schema that models EMA questions and the necessary attributes to effectively and efficiently integrate voice as a new EMA modality. Our schema allows user experience researchers to write simple rules that can be rendered at run-time, instead of having to edit the source code. We showcase an example EMA survey implemented with our schema, which can run on multiple voice-only and voice-first devices. We believe that our work will accelerate the iterative prototyping and design process of real-world voice-based EMA data collection platforms.
A two-stage information retrieval system based on interactive multimodal genetic algorithm for query weight optimization

Abstract Query weight optimization, which aims to find an optimal combination of the weights of query terms for sorting relevant documents, is an important topic in the information retrieval system. Due to the huge search space, the query optimization problem is intractable, and evolutionary algorithms have become one popular approach. But as the size of the database grows, traditional retrieval approaches may return a lot of results, which leads to low efficiency and poor practicality. To solve this problem, this paper proposes a two-stage information retrieval system based on an interactive multimodal genetic algorithm (IMGA) for a query weight optimization system. The proposed IMGA has two stages: quantity control and quality optimization. In the quantity control stage, a multimodal genetic algorithm with the aid of the niching method selects multiple promising combinations of query terms simultaneously by which the numbers of retrieved documents are controlled in an appropriate range. In the quality optimization stage, an interactive genetic algorithm is designed to find the optimal query weights so that the most user-friendly document retrieval sequence can be yielded. Users’ feedback information will accelerate the optimization process, and a genetic algorithm (GA) performs interactively with the action of relevance feedback mechanism. Replacing user evaluation, a mathematical model is built to evaluate the fitness values of individuals. In the proposed two-stage method, not only the number of returned results can be controlled, but also the quality and accuracy of retrieval can be improved. The proposed method is run on the database which with more than 2000 documents. The experimental results show that our proposed method outperforms several state-of-the-art query weight optimization approaches in terms of the precision rate and the recall rate.
When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban

Community-level bans are a common tool against groups that enable online harassment and harmful speech.Unfortunately, the efficacy of community bans has only been partially studied and with mixed results.Here, we provide a flexible unsupervised methodology to identify in-group language and track user activity on Reddit both before and after the ban of a community (subreddit).We use a simple word frequency divergence to identify uncommon words overrepresented in a given community, not as a proxy for harmful speech but as a linguistic signature of the community.We apply our method to 15 banned subreddits, and find that community response is heterogeneous between subreddits and between users of a subreddit.Top users were more likely to become less active overall, while random users often reduced use of in-group language without decreasing activity.Finally, we find some evidence that the effectiveness of bans aligns with the content of a community.Users of dark humor communities were largely unaffected by bans while users of communities organized around white supremacy and fascism were the most affected.Altogether, our results show that bans do not affect all groups or users equally, and pave the way to understanding the effect of bans across communities.
Automated Safety Verification of Programs Invoking Neural Networks

Abstract State-of-the-art program-analysis techniques are not yet able to effectively verify safety properties of heterogeneous systems, that is, systems with components implemented using diverse technologies. This shortcoming is pinpointed by programs invoking neural networks despite their acclaimed role as innovation drivers across many application areas. In this paper, we embark on the verification of system-level properties for systems characterized by interaction between programs and neural networks. Our technique provides a tight two-way integration of a program and a neural-network analysis and is formalized in a general framework based on abstract interpretation. We evaluate its effectiveness on 26 variants of a widely used, restricted autonomous-driving benchmark.
Leveraging Multimodal Semantic Fusion for Gastric Cancer Screening via Hierarchical Attention Mechanism

Gastroscopy is a widely adopted method for locating gastric lesions and performing the early screening and diagnosis of gastric cancer (GC). However, the effectiveness of traditional GC screening methods depends on the medical skills of the gastroscopy specialist. A lack of knowledge and experience may lead to misdiagnosis and mistreatment, especially in small-scale hospitals. Recently, there has been a significant increase in studies on data-driven computer-aided diagnosis techniques. In this article, we propose a novel intelligent decision-making method for GC screening (ID-GCS), a multimodal semantic fusion-based data-driven decision-making system. ID-GCS exploits a hybrid attention mechanism to extract textual semantics from multimodal gastroscopy reports and performs semantic fusion to integrate the semantics of textual gastroscopy reports and images, resulting in improved interpretability of gastroscopy findings. We evaluated ID-GCS using a real gastroscopy report dataset, and experimental results show that compared with state-of-the-art methods, ID-GCS achieves better sensitivity and accuracy in GC screening.
Virtual Sketch using Open CV

Virtual Sketch is in where we can draw by just capturing the motion of a colored marker with a camera. One colored object at the tip of the finger is mainly used as the marker. We are here now, using the techniques of computer vision in open cv to build this project. The required language for this project is python due to its more exhaustive libraries and easy to make use of the syntax and but understanding the basics as well as it can be implemented in any open cv supported languages The colour tracking and detection processes are used to achieve the goal of this project. The color marker here used is detected and mask is produced. The next steps of morphological operations on the mask produced those are Erosion and Dilation. Erosion makes the impurities present in the mask to get reduced and Dilation further regains the eroded main mask.
3-D visualization of transparent fluid flows from snapshot light field data

Abstract This paper presents the use of light field data, recorded in a snapshot from a single plenoptic camera, for 3-D visualization of transparent fluid flows. We demonstrate the transfer of light field deconvolution, a method so far used only in microscopy, to macroscopic scales with a photographic setup. This technique is suitable for optically thin media without any additional particles or tracers and allows volumetric investigation of non-stationary flows with a simple single camera setup. An experimental technique for the determination of the shift-variant point spread functions is presented, which is a key for applications using a photographic optical system. The paper shows results from different test cases with increasing complexity. Reconstruction of the 3-D positions of randomly distributed light points demonstrates the achievable high accuracy of the technique. Gas flames and droplets of a fluorescent liquid show the feasibility of the proposed method for the visualization of transparent, luminous flows. The visualizations exhibit high quality and resolution in low-contrast flows, where standard plenoptic software based on computer vision fails. Axial resolution depends on the data and is about an order of magnitude lower than the lateral resolution for simple point objects. The technique also allows the time-resolved analysis of flow structures and the generation of 3D3C-velocity fields from a sequence of exposures. Graphical Abstract
Localized Graph Collaborative Filtering

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)Localized Graph Collaborative FilteringYiqi Wang, Chaozhuo Li, Mingzheng Li, Wei Jin, Yuming Liu, Hao Sun, Xing Xie, and Jiliang TangYiqi Wang, Chaozhuo Li, Mingzheng Li, Wei Jin, Yuming Liu, Hao Sun, Xing Xie, and Jiliang Tangpp.540 - 548Chapter DOI:https://doi.org/10.1137/1.9781611977172.61PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract User-item interactions in recommendations can be naturally denoted as a user-item bipartite graph. Given the success of graph neural networks (GNNs) in graph representation learning, GNN-based Collaborative Filtering (CF) methods have been proposed to advance recommender systems. These methods often make recommendations based on the learned user and item embeddings. However, we found that they do not perform well with sparse user-item graphs which are quite common in real-world recommendations. Therefore, in this work, we introduce a novel perspective to build GNN-based CF methods for recommendations which leads to the proposed framework Localized Graph Collaborative Filtering (LGCF). One key advantage of LGCF is that it does not need to learn embeddings for each user and item, which is challenging in sparse scenarios. Alternatively, LGCF aims at encoding useful CF information into a localized graph and making recommendations based on such graph. Extensive experiments on various datasets validate the effectiveness of LGCF, especially in sparse scenarios. Furthermore, empirical results demonstrate that LGCF provides complementary information to the embedding-based CF model which can be utilized to boost recommendation performance. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
Distributed quantum phase sensing for arbitrary positive and negative weights

Estimation of a global parameter defined as a weighted linear combination of unknown multiple parameters can be enhanced by using quantum resources. Advantageous quantum strategies may vary depending on the weight distribution, requiring the study of an optimal scheme achieving a maximal quantum advantage for a given sensing scenario. In this work, we propose a Heisenberg-limited distributed quantum phase sensing scheme using Gaussian states for an arbitrary distribution of the weights with positive and negative signs. The proposed scheme exploits entanglement of Gaussian states only among the modes assigned with equal signs of the weights, but separates the modes with opposite weight signs. We show that the estimation precision of the scheme exhibits the Heisenberg scaling in the mean photon number and it can be achieved by injecting two single-mode squeezed states into the respective linear beam-splitter networks and performing homodyne detection on them in the absence of loss. Interestingly, the proposed scheme is proven to be optimal for Gaussian probe states with zero displacement. We also provide an intuitive understanding of our results by focusing on the two-mode case, in comparison with the cases using non-Gaussian probe states. We expect this work to motivate further studies on quantum-enhanced distributed sensing schemes considering various types of physical parameters with an arbitrary weight distribution.
An Exhaustive Approach to Detecting Transient Execution Side Channels in RTL Designs of Processors

Hardware (HW) security issues have been emerging at an alarming rate in recent years. Transient execution attacks, such as Spectre and Meltdown, in particular, pose a genuine threat to the security of modern computing systems. Despite recent advances, understanding the intricate implications of microarchitectural design decisions on processor security remains a great challenge and has caused a number of update cycles in the past. This papers addresses the need for a new approach to HW sign-off verification which guarantees the security of processors at the Register Transfer Level (RTL). To this end, we introduce a formal definition of security with respect to transient execution attacks, formulated as a HW property. We present a formal proof methodology based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Unique Program Execution Checking (UPEC)</i> which can be used to systematically detect all vulnerabilities to transient execution attacks in RTL designs. UPEC does not exploit any a priori knowledge on known attacks and can therefore detect also vulnerabilities based on new, so far unknown, types of channels. This is demonstrated by two new attack scenarios discovered in our experiments with UPEC. UPEC scales to a wide range of HW designs, including in-order processors (RocketChip), pipelines with out-of-order writeback (Ariane), and processors with deep out-of-order speculative execution (BOOM). To the best of our knowledge, UPEC is the first RTL verification technique that exhaustively covers transient execution side channels in processors of realistic complexity.
Relating Apartness and Bisimulation

A bisimulation for a coalgebra of a functor on the category of sets can be described via a coalgebra in the category of relations, of a lifted functor. A final coalgebra then gives rise to the coinduction principle, which states that two bisimilar elements are equal. For polynomial functors, this leads to well-known descriptions. In the present paper we look at the dual notion of "apartness". Intuitively, two elements are apart if there is a positive way to distinguish them. Phrased differently: two elements are apart if and only if they are not bisimilar. Since apartness is an inductive notion, described by a least fixed point, we can give a proof system, to derive that two elements are apart. This proof system has derivation rules and two elements are apart if and only if there is a finite derivation (using the rules) of this fact. We study apartness versus bisimulation in two separate ways. First, for weak forms of bisimulation on labelled transition systems, where silent (tau) steps are included, we define an apartness notion that corresponds to weak bisimulation and another apartness that corresponds to branching bisimulation. The rules for apartness can be used to show that two states of a labelled transition system are not branching bismilar. To support the apartness view on labelled transition systems, we cast a number of well-known properties of branching bisimulation in terms of branching apartness and prove them. Next, we also study the more general categorical situation and show that indeed, apartness is the dual of bisimilarity in a precise categorical sense: apartness is an initial algebra and gives rise to an induction principle. In this analogy, we include the powerset functor, which gives a semantics to non-deterministic choice in process-theory.
Automatically Paraphrasing via Sentence Reconstruction and Round-trip Translation

Paraphrase generation plays key roles in NLP tasks such as question answering, machine translation, and information retrieval. In this paper, we propose a novel framework for paraphrase generation. It simultaneously decodes the output sentence using a pretrained wordset-to-sequence model and a round-trip translation model. We evaluate this framework on Quora, WikiAnswers, MSCOCO and Twitter, and show its advantage over previous state-of-the-art unsupervised methods and distantly-supervised methods by significant margins on all datasets. For Quora and WikiAnswers, our framework even performs better than some strongly supervised methods with domain adaptation. Further, we show that the generated paraphrases can be used to augment the training data for machine translation to achieve substantial improvements.
Android-based object recognition application for visually impaired

Detecting objects in real-time and converting them into an audio output was a challenging task. Recent advancement in computer vision has allowed the development of various real-time object detection applications. This paper describes a simple android app that would help the visually impaired people in understanding their surroundings. The information about the surrounding environment was captured through a phone’s camera where real-time object recognition through tensorflow’s object detection API was done. The detected objects were then converted into an audio output by using android’s text-to-speech library. Tensorflow lite made the offline processing of complex algorithms simple. The overall accuracy of the proposed system was found to be approximately 90%.