Reviewerly/exHarmony huggingface dataset:
PASS: A Parameter Audit-based Secure and Fair Federated Learning Scheme against Free-Rider Attack

Federated Learning (FL) as a secure distributed learning framework gains interests in Internet of Things (IoT) due to its capability of protecting the privacy of participant data. However, traditional FL systems are vulnerable to Free-Rider (FR) attacks, which causes unfairness, privacy leakage and inferior performance to FL systems. The prior defense mechanisms against FR attacks assumed that malicious clients (namely, adversaries) declare less than 50% of the total amount of clients. Moreover, they aimed for Anonymous FR (AFR) attacks and lost effectiveness in resisting Selfish FR (SFR) attacks. In this paper, we propose a Parameter Audit-based Secure and fair federated learning Scheme (PASS) against FR attack. PASS has the following key features: (a) prevent from privacy leakage with less accuracy loss; (b) be effective in countering both AFR and SFR attacks; (c) work well no matter whether AFR and SFR adversaries occupy the majority of clients or not. Extensive experimental results validate that PASS: (a) has the same level as the State-Of-The-Art method in mean square error against privacy leakage; (b) defends against AFR and SFR attacks in terms of a higher defense success rate, lower false positive rate, and higher F1-score; (c) is still effective where adversaries exceed 50%, with F1-score 89% against AFR attack and F1-score 87% against SFR attack. Note that PASS produces no negative effect on FL accuracy when there is no FR adversary.
On the Construction of Averaged Deep Denoisers for Image Regularization

Plug-and-Play (PnP) and Regularization-by-Denoising (RED) are recent paradigms for image reconstruction that leverage the power of modern denoisers for image regularization. In particular, they have been shown to deliver state-of-the-art reconstructions with CNN denoisers. Since the regularization is performed in an ad-hoc manner, understanding the convergence of PnP and RED has been an active research area. It was shown in recent works that iterate convergence can be guaranteed if the denoiser is averaged or nonexpansive. However, integrating nonexpansivity with gradient-based learning is challenging, the core issue being that testing nonexpansivity is intractable. Using numerical examples, we show that existing CNN denoisers tend to violate the nonexpansive property, which can cause PnP or RED to diverge. In fact, algorithms for training nonexpansive denoisers either cannot guarantee nonexpansivity or are computationally intensive. In this work, we construct contractive and averaged image denoisers by unfolding splitting-based optimization algorithms applied to wavelet denoising and demonstrate that their regularization capacity for PnP and RED can be matched with CNN denoisers. To our knowledge, this is the first work to propose a simple framework for training contractive denoisers using network unfolding.
Ultrafast quantum key distribution using fully parallelized quantum channels

The field of quantum information processing offers secure communication protected by the laws of quantum mechanics and is on the verge of finding wider application for information transfer of sensitive data. To overcome the obstacle of inadequate cost-efficiency, extensive research is being done on the many components required for high data throughput using quantum key distribution (QKD). Aiming for an application-oriented solution, we report on the realization of a multichannel QKD system for plug-and-play high-bandwidth secure communication at telecom wavelength. For this purpose, a rack-sized multichannel superconducting nanowire single photon detector (SNSPD) system, as well as a highly parallelized time-correlated single photon counting (TCSPC) unit have been developed and linked to an FPGA-controlled QKD evaluation setup allowing for continuous operation and achieving high secret key rates using a coherent-one-way protocol.
Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet Text

The wide use of social media and digital technologies facilitates sharing various news and information about events and activities. Despite sharing positive information misleading and false information is also spreading on social media. There have been efforts in identifying such misleading information both manually by human experts and automatic tools. Manual effort does not scale well due to the high volume of information, containing factual claims, are appearing online. Therefore, automatically identifying check-worthy claims can be very useful for human experts. In this study, we describe our participation in Subtask-1A: Check-worthiness of tweets (English, Dutch and Spanish) of CheckThat! lab at CLEF 2022. We performed standard preprocessing steps and applied different models to identify whether a given text is worthy of fact checking or not. We use the oversampling technique to balance the dataset and applied SVM and Random Forest (RF) with TF-IDF representations. We also used BERT multilingual (BERT-m) and XLM-RoBERTa-base pre-trained models for the experiments. We used BERT-m for the official submissions and our systems ranked as 3rd, 5th, and 12th in Spanish, Dutch, and English, respectively. In further experiments, our evaluation shows that transformer models (BERT-m and XLM-RoBERTa-base) outperform the SVM and RF in Dutch and English languages where a different scenario is observed for Spanish.
A unified relational semantics for intuitionistic logic, basic propositional logic and orthologic with strict implication

In this paper, by slightly generalizing the notion of 'proposition' in 'Propositional Logic and Modal Logic - A Connection via Relational Semantics' by Shengyang Zhong, we propose a relational semantics of propositional language with bottom, conjunction and imlication, which unifies the relational semantics of intuitionistic logic, Visser's basic propositional logic and orthologic with strict implication. We study the semantic and syntactic consequence relations and prove the soundness and completeness theorems for eight propositional logics.
First-order logic with self-reference

We consider an extension of first-order logic with a recursion operator that corresponds to allowing formulas to refer to themselves. We investigate the obtained language under two different systems of semantics, thereby obtaining two closely related but different logics. We provide a natural deduction system that is complete for validities for both of these logics, and we also investigate a range of related basic decision problems. For example, the validity problems of the two-variable fragments of the logics are shown coNexpTime-complete, which is in stark contrast with the high undecidability of two-variable logic extended with least fixed points. We also argue for the naturalness and benefits of the investigated approach to recursion and self-reference by, for example, relating the new logics to Lindstrom's Second Theorem.
On the Selection of Tuning Parameters for Patch-Stitching Embedding Methods

While classical scaling, just like principal component analysis, is parameter-free, other methods for embedding multivariate data require the selection of one or several tuning parameters. This tuning can be difficult due to the unsupervised nature of the situation. We propose a simple, almost obvious, approach to supervise the choice of tuning parameter(s): minimize a notion of stress. We apply this approach to the selection of the patch size in a prototypical patch-stitching embedding method, both in the multidimensional scaling (aka network localization) setting and in the dimensionality reduction (aka manifold learning) setting. In our study, we uncover a new bias--variance tradeoff phenomenon.
A Flexible Schema-Guided Dialogue Management Framework: From Friendly Peer to Virtual Standardized Cancer Patient

A schema-guided approach to dialogue management has been shown in recent work to be effective in creating robust customizable virtual agents capable of acting as friendly peers or task assistants. However, successful applications of these methods in open-ended, mixed-initiative domains remain elusive -- particularly within medical domains such as virtual standardized patients, where such complex interactions are commonplace -- and require more extensive and flexible dialogue management capabilities than previous systems provide. In this paper, we describe a general-purpose schema-guided dialogue management framework used to develop SOPHIE, a virtual standardized cancer patient that allows a doctor to conveniently practice for interactions with patients. We conduct a crowdsourced evaluation of conversations between medical students and SOPHIE. Our agent is judged to produce responses that are natural, emotionally appropriate, and consistent with her role as a cancer patient. Furthermore, it significantly outperforms an end-to-end neural model fine-tuned on a human standardized patient corpus, attesting to the advantages of a schema-guided approach.
Single Model Uncertainty Estimation via Stochastic Data Centering

We are interested in estimating the uncertainties of deep neural networks, which play an important role in many scientific and engineering problems. In this paper, we present a striking new finding that an ensemble of neural networks with the same weight initialization, trained on datasets that are shifted by a constant bias gives rise to slightly inconsistent trained models, where the differences in predictions are a strong indicator of epistemic uncertainties. Using the neural tangent kernel (NTK), we demonstrate that this phenomena occurs in part because the NTK is not shift-invariant. Since this is achieved via a trivial input transformation, we show that this behavior can therefore be approximated by training a single neural network -- using a technique that we call $\Delta-$UQ -- that estimates uncertainty around prediction by marginalizing out the effect of the biases during inference. We show that $\Delta-$UQ's uncertainty estimates are superior to many of the current methods on a variety of benchmarks -- outlier rejection, calibration under distribution shift, and sequential design optimization of black box functions. Code for $\Delta-$UQ can be accessed at https://github.com/LLNL/DeltaUQ
Efficient Human Vision Inspired Action Recognition using Adaptive Spatiotemporal Sampling

Adaptive sampling that exploits the spatiotemporal redundancy in videos is critical for always-on action recognition on wearable devices with limited computing and battery resources. The commonly used fixed sampling strategy is not context-aware and may under-sample the visual content, and thus adversely impacts both computation efficiency and accuracy. Inspired by the concepts of foveal vision and pre-attentive processing from the human visual perception mechanism, we introduce a novel adaptive spatiotemporal sampling scheme for efficient action recognition. Our system pre-scans the global scene context at low-resolution and decides to skip or request high-resolution features at salient regions for further processing. We validate the system on EPIC-KITCHENS and UCF-101 datasets for action recognition, and show that our proposed approach can greatly speed up inference with a tolerable loss of accuracy compared with those from state-of-the-art baselines. Source code is available in https://github.com/knmac/adaptive_spatiotemporal.
Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning

Despite achieving state-of-the-art zero-shot performance, existing vision-language models still fall short of few-shot transfer ability on domain-specific problems. Classical fine-tuning often fails to prevent highly expressive models from exploiting spurious correlations. Although model-agnostic meta-learning (MAML) presents as a natural alternative for few-shot transfer learning, the expensive computation due to implicit second-order optimization limits its use on large-scale vision-language models such as CLIP. While much literature has been devoted to exploring alternative optimization strategies, we identify another essential aspect towards effective few-shot transfer learning, task sampling, which is previously only be viewed as part of data pre-processing in MAML. To show the impact of task sampling, we propose a simple algorithm, Model-Agnostic Multitask Fine-tuning (MAMF), which differentiates classical fine-tuning only on uniformly sampling multiple tasks. Despite its simplicity, we show that MAMF consistently outperforms classical fine-tuning on five few-shot vision-language classification tasks. We further show that the effectiveness of the bi-level optimization in MAML is highly sensitive to the zero-shot performance of a task in the context of few-shot vision-language classification. The goal of this paper is to provide new insights on what makes few-shot learning work, and encourage more research into investigating better task sampling strategies.
Recur, Attend or Convolve? On Whether Temporal Modeling Matters for Cross-Domain Robustness in Action Recognition

Most action recognition models today are highly parameterized, and evaluated on datasets with appearance-wise distinct classes. It has also been shown that 2D Convolutional Neural Networks (CNNs) tend to be biased toward texture rather than shape in still image recognition tasks, in contrast to humans. Taken together, this raises suspicion that large video models partly learn spurious spatial texture correlations rather than to track relevant shapes over time to infer generalizable semantics from their movement. A natural way to avoid parameter explosion when learning visual patterns over time is to make use of recurrence. Biological vision consists of abundant recurrent circuitry, and is superior to computer vision in terms of domain shift generalization. In this article, we empirically study whether the choice of low-level temporal modeling has consequences for texture bias and cross-domain robustness. In order to enable a light-weight and systematic assessment of the ability to capture temporal structure, not revealed from single frames, we provide the Temporal Shape (TS) dataset, as well as modified domains of Diving48 allowing for the investigation of spatial texture bias in video models. The combined results of our experiments indicate that sound physical inductive bias such as recurrence in temporal modeling may be advantageous when robustness to domain shift is important for the task.
Edit Distance in Near-Linear Time: it's a Constant Factor

We present an algorithm for approximating the edit distance between two strings of length $n$ in time $n^{1+\varepsilon}$ up to a constant factor, for any $\varepsilon>0$. Our result completes a research direction set forth in the recent breakthrough paper [Chakraborty-Das-Goldenberg-Kouck\`y-Saks, FOCS'18], which showed the first constant-factor approximation algorithm with a (strongly) sub-quadratic running time. The recent results of [Kouck\`y-Saks, STOC'20] and [Brakensiek-Rubinstein, STOC'20] have shown near-linear time algorithms that obtain an additive approximation, near-linear in $n$ (equivalently, constant-factor approximation when the edit distance value is close to $n$). In contrast, our algorithm obtains a constant-factor approximation in near-linear time for any input strings. In contrast to prior algorithms, which are mostly recursing over smaller substrings, our algorithm gradually smoothes out the local contribution to the edit distance over progressively larger substrings. To accomplish this, we iteratively construct a distance oracle data structure for the metric of edit distance on all substrings of input strings, of length $n^{i\varepsilon}$ for $i=0,1,\ldots,1/\varepsilon$. The distance oracle approximates the edit distance over these substrings in a certain average sense, just enough to estimate the overall edit distance.
Towards automated feature engineering for credit card fraud detection using multi-perspective HMMs

Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this framework, we model a sequence of credit card transactions from three different perspectives, namely (i) The sequence contains or doesn't contain a fraud (ii) The sequence is obtained by fixing the card-holder or the payment terminal (iii) It is a sequence of spent amount or of elapsed time between the current and previous transactions. Combinations of the three binary perspectives give eight sets of sequences from the (training) set of transactions. Each one of these sequences is modelled with a Hidden Markov Model (HMM). Each HMM associates a likelihood to a transaction given its sequence of previous transactions. These likelihoods are used as additional features in a Random Forest classifier for fraud detection. Our multiple perspectives HMM-based approach offers automated feature engineering to model temporal correlations so as to improve the effectiveness of the classification task and allows for an increase in the detection of fraudulent transactions when combined with the state of the art expert based feature engineering strategy for credit card fraud detection. In extension to previous works, we show that this approach goes beyond ecommerce transactions and provides a robust feature engineering over different datasets, hyperparameters and classifiers. Moreover, we compare strategies to deal with structural missing values.
Design a knowledge base system (KBOSR) for enhancing software reusability

As a result of the development of software engineering , especially with regard to improving the software systems within a short time to enhance the development process and reduce cost as much as possible . Software reusability is one of the most important software engineering metrics which aim to reduce the effort and time in the development process . It is the process of using pre-existing software components for implementing and updating new software systems , thereby reducing effort , time and cost in addition to increasing the quality and productivity of software systems . Many factors affect the reusability of a software , of these factors , coupling and cohesion are used in this search for the aforementioned purpose . Reusability of classes is determined based on coupling and cohesion results . Cohesion measures the intra-dependability of functions and variables inside the class , while coupling measures the inter-dependability among classes in the same package . The proposed expert system also used for computing coupling , cohesion and reusability status , and produce suggestions to enhance the reusability status of unreusable classes by offering intelligence advices which depending on the weakness of the tested codes . Proposed knowledge Base System (KBOSR) contains (61) rules to provide and demonstrate why the items are not reusable by displaying code weaknesses , and to provide advice, guidance and modifications to the code for making the product reusable and displaying these guidelines and instructions on user interfaces in a clear and easy-to-understand way, KBOSR is the first knowledge base system to determine the status of reuse and improve it . The knowledge base was tested using 13 classes were distributed in two packages , first package contains 5 classes , second package contains 8 classes , coupling and cohesion values of classes are computed . Coupling , cohesion and reusability status of these classes are determined , then , by proposed expert system . Finally , suggestions of improvement of unresable classes are displayed . The results of testing process have proved the strength and validity of the proposed knowledge base in determining reusability status of the software components , and the accuracy of the advices that were suggested , these advices are practically applied on the codes of interest and the modified codes proved to be highly reusable .
ORBSLAM-Atlas: a robust and accurate multi-map system

We propose ORBSLAM-Atlas, a system able to handle an unlimited number of disconnected sub-maps, that includes a robust map merging algorithm able to detect sub-maps with common regions and seamlessly fuse them. The outstanding robustness and accuracy of ORBSLAM are due to its ability to detect wide-baseline matches between keyframes, and to exploit them by means of non-linear optimization, however it only can handle a single map. ORBSLAM-Atlas brings the wide-baseline matching detection and exploitation to the multiple map arena. The result is a SLAM system significantly more general and robust, able to perform multi-session mapping. If tracking is lost during exploration, instead of freezing the map, a new sub-map is launched, and it can be fused with the previous map when common parts are visited. Our criteria to declare the camera lost contrast with previous approaches that simply count the number of tracked points, we propose to discard also inaccurately estimated camera poses due to bad geometrical conditioning. As a result, the map is split into more accurate sub-maps, that are eventually merged in a more accurate global map, thanks to the multi-mapping capabilities. We provide extensive experimental validation in the EuRoC datasets, where ORBSLAM-Atlas obtains accurate monocular and stereo results in the difficult sequences where ORBSLAM failed. We also build global maps after multiple sessions in the same room, obtaining the best results to date, between 2 and 3 times more accurate than competing multi-map approaches. We also show the robustness and capability of our system to deal with dynamic scenes, quantitatively in the EuRoC datasets and qualitatively in a densely populated corridor where camera occlusions and tracking losses are frequent.
The Effectiveness of Talking Chips Model Aided by Visual Media Toward Javanesse Language Learning Outcomes

Based on the results of observation, interview, and the data of learning outcomes, it was obtained that Javanesse language learning outcomes was less maximal, the model used by teacher was unvaried, and the limitation of the media used. This research aimed to examine the effectiveness of talking chips model aided by visual-media towards Javanesse language learning outcomes on fifth grade students of Gugus Cendana Elementary School, Blora Regency. This research used experimental quantitative method focused on nonequivalent control group design. The technique for choosing the sample was cluster random sampling. The total sample used in this research was 36 students. The method of collecting the data was by giving test, observation, interview, and documentation. The result of hypothesis test showed that the score of t count=3.067, and t table= 2.032, t count &gt; t table (3.067 &gt; 2.032), it meant that talking chips model aided by visual-media more effective for the Javanesse language learning outcomes. The result of n-gain on experimental group was higher than the controlled one. The score of n-gain on controlled group was 0.029 which belonged to the low level. Whereas, the score of n-gain on experimental group was 0.52 which belonged to the medium level. The result of observation on studentsâ€™ activity showed that the score average of experimental group was higher than the controlled one. While the experimental group stood for 76.5%, the controlled group stood for 62%. The conclusion of this research pointed out that talking chips model aided by visual-media was effective for the Javanesse language learning outcomes on the fifth grade students of Gugus Cendana Elementary School, Blora Regency. In this case, the researcher suggest that talking chips model aided by visual-media can be used as an alternative of learning model which can be applied to enhance the studentsâ€™ activity, giving the same chance for the students in the learning process. Therefore, hopefully the teacher is able to create an interesting and wonderful learning atmosphere for the students; so that the students are able to achieve the learning objectives.