Reviewerly/exHarmony huggingface dataset:
A Simplified Benchmark for Ambiguous Explanations of Knowledge Graph Link Prediction Using Relational Graph Convolutional Networks (Student Abstract)

Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. We propose and evaluate a method, including a dataset, to benchmark explanation methods on the task of explainable link prediction using RGCNs.
Bridging the Gap: Using Deep Acoustic Representations to Learn Grounded Language from Percepts and Raw Speech

Learning to understand grounded language, which connects natural language to percepts, is a critical research area. Prior work in grounded language acquisition has focused primarily on textual inputs. In this work, we demonstrate the feasibility of performing grounded language acquisition on paired visual percepts and raw speech inputs. This will allow human-robot interactions in which language about novel tasks and environments is learned from end-users, reducing dependence on textual inputs and potentially mitigating the effects of demographic bias found in widely available speech recognition systems. We leverage recent work in self-supervised speech representation models and show that learned representations of speech can make language grounding systems more inclusive towards specific groups while maintaining or even increasing general performance.
A Calculus for Computing Structured Justifications for Election Outcomes

In the context of social choice theory, we develop a tableau-based calculus for reasoning about voting rules. This calculus can be used to obtain structured explanations for why a given set of axioms justifies a given election outcome for a given profile of voter preferences. We then show how to operationalise this calculus, using a combination of SAT solving and answer set programming, to arrive at a flexible framework for presenting human-readable justifications to users.
Assessing the quality of cytopathology whole slide imaging for education from archived cases

Many institutions have cytopathology case archives for education. Unfortunately, these slides deteriorate over time and have limited accessibility. Whole slide imaging (WSI) can overcome these limitations. However, suboptimal image quality and scanning effort are barriers. We selected 123 slides from cytopathology study sets for WSI scanning at 400x magnification without z-stacking. The Ventana DP 200 scanner and Virtuoso software were used. Slides were scanned in 2 rounds: the first round of slides was prepared for scanning with light cleaning, and the second round was performed only on slides that had unacceptable WSI quality after thorough cleaning. Slides were assessed with a 4-tier grading system created by the authors. Time to scan each slide was recorded. Within the first round, 96 of 123 (78%) slides scanned were determined to be of acceptable quality. After the second round of scanning, in total, 118 of 123 (95.9%) slides were determined to be of acceptable quality. The average time needed to scan each slide was 213 seconds. The majority of slides scanned were of acceptable quality in the first round of scanning. After cleaning and rescanning, nearly every slide investigated was of acceptable quality. The primary objective is to provide other institutions that may be considering a similar project a benchmark so that they know what to expect in terms of slide scan success rate and the amount of time needed to digitize slides for educational archiving. This pilot study demonstrates the feasibility of using WSI for cytology education cases.
Probabilistic Approach for Identifying Longest Fuzzy Critical Path

This paper, considers the occurring problem of fuzzy project network either in some or whole activities which areinterrupted. A probability density function is constructed from the activities of the membership of the fuzzy set. Then an approach is proposed for identifying the longest critical path, by determining in each activity thecriticality index depending on the costs yielded from project lateness caused by the interruption activities. A case study is presented and simulated to explain our approach.
Deep Learning Based Side Channel Attacks on Lightweight Cryptography (Student Abstract)

Computing devices continue to be increasingly spread out within our everyday environments. Computers are embedded into everyday devices in order to serve the functionality of electronic components or to enable new services in their own right. Existing Substitution-Permutation Network (SPN) ciphers, such as the Advanced Encryption Standard (AES), are not suitable for devices where memory, power consumption or processing power is limited. Lightweight SPN ciphers, such as GIFT-128 provide a solution for running cryptography on low resource devices. The GIFT-128 cryptographic scheme is a building block for GIFT-COFB (Authenticated Encryption with Associated Data), one of the finalists in the ongoing NIST lightweight cryptography standardization process (NISTIR 8369). Determination of an adequate level of security and providing subsequent mechanisms to achieve it, is one of the most pressing problems regarding embedded computing devices. In this paper we present experimental results and comparative study of Deep Learning (DL) based Side Channel Attacks on lightweight GIFT-128. To our knowledge, this is the first study of the security of GIFT-128 against DL-based SCA attacks.
Bi-volution: A Static and Dynamic Coupled Filter

Dynamic convolution has achieved significant gain in performance and computational complexity, thanks to its powerful representation capability given limited filter number/layers. However, SOTA dynamic convolution operators are sensitive to input noises (e.g., Gaussian noise, shot noise, e.t.c.) and lack sufficient spatial contextual information in filter generation. To alleviate this inherent weakness, we propose a lightweight and heterogeneous-structure (i.e., static and dynamic) operator, named Bi-volution. On the one hand, Bi-volution is designed as a dual-branch structure to fully leverage complementary properties of static/dynamic convolution, which endows Bi-volution more robust properties and higher performance. On the other hand, the Spatial Augmented Kernel Generation module is proposed to improve the dynamic convolution, realizing the learning of spatial context information with negligible additional computational complexity. Extensive experiments illustrate that the ResNet-50 equipped with Bi-volution achieves a highly competitive boost in performance (+2.8% top-1 accuracy on ImageNet classification, +2.4% box AP and +2.2% mask AP on COCO detection and instance segmentation) while maintaining extremely low FLOPs (i.e., ResNet50@2.7 GFLOPs). Furthermore, our Bi-volution shows better robustness than dynamic convolution against various noise and input corruptions. Our code is available at https://github.com/neuralchen/Bivolution.
Ta’lim sohasidagi korruptsiyaning o‘ziga xos xususiyatlari va turlari

Korruptsiya ommaviy xarakterga ega va keng tarqalish sohasi bilan ajralib turadigan murakkab ijtimoiy hodisa sifatida ta’lim tizimiga ham kirib keldi. Ishonch bilan ayta olamizki, aynan ta’lim tizimi korruptsiyaga moyil bo‘lgan sohalardan biri bo‘lib, faoliyatining o‘ziga xos xususiyatlariga ko‘ra, unda o‘qituvchilar va o‘quvchilar o‘rtasida va muassasa xodimlari uning ma’muriyati bilan ham vertikal, ham gorizontal munosabatlar mavjudligi sababli ushbu salbiy hodisaga moyil bo‘ladi. Ta’lim sohasidagi korruptsiya umumiy korruptsiya tushunchasining barcha xususiyatlariga ega. Biroq, korruptsiyaga oid huquqbuzarliklarning o‘z vaqtida oldini olish uchun amaliyotda qo‘llanilishi mumkin bo‘lgan tizimli bilimlarni shakllantirish uchun uning bir qator xarakterli xususiyatlarini aniqlash kerak ko‘rinadi. Ta’lim sohasidagi korruptsiyaga bag‘ishlangan ko‘plab ishlarga qaramay, uning xarakterli xususiyatlari, shakllari, turlari masalasi ochiqligicha qolmoqda. Muallif ushbu maqolada ta’lim sohasidagi korrupsiyani bir qancha mezonlarga ko‘ra tasniflashga harakat qilib, ushbu turdagi tadqiqotlarni o‘tkazishning ahamiyatini bayon qilgan.
Business Partnership Management of Business Entities: Systemic and Process Approaches

The article features systemic and process approaches to business partnership. The authors explored the category of business partnership as a process and as a system, as well as the place of business partnership management in this system and the peculiarities of this process. They developed a new system for business partnership management and two algorithms, i.e., for the business partnership process and for its strategic management. The study involved standard methods of analysis. The recommendations can be used in managing business partnerships of business entities.
MMAN: Metapath Based Multi-Level Graph Attention Networks for Heterogeneous Network Embedding (Student Abstract)

Current Heterogeneous Network Embedding (HNE) models can be roughly divided into two types, i.e., relation-aware and metapath-aware models. However, they either fail to represent the non-pairwise relations in heterogeneous graph, or only capable of capturing local information around target node. In this paper, we propose a metapath based multilevel graph attention networks (MMAN) to jointly learn node embeddings on two substructures, i.e., metapath based graphs and hypergraphs extracted from original heterogeneous graph. Extensive experiments on three benchmark datasets for node classification and node clustering demonstrate the superiority of MMAN over the state-of-the-art works.
Enhancing Counterfactual Classification Performance via Self-Training

Unlike traditional supervised learning, in many settings only partial feedback is available. We may only observe outcomes for the chosen actions, but not the counterfactual outcomes associated with other alternatives. Such settings encompass a wide variety of applications including pricing, online marketing and precision medicine. A key challenge is that observational data are influenced by historical policies deployed in the system, yielding a biased data distribution. We approach this task as a domain adaptation problem and propose a self-training algorithm which imputes outcomes with categorical values for finite unseen actions in the observational data to simulate a randomized trial through pseudolabelling, which we refer to as Counterfactual Self-Training (CST). CST iteratively imputes pseudolabels and retrains the model. In addition, we show input consistency loss can further improve CST performance which is shown in recent theoretical analysis of pseudolabelling. We demonstrate the effectiveness of the proposed algorithms on both synthetic and real datasets.
Admissible Policy Teaching through Reward Design

We study reward design strategies for incentivizing a reinforcement learning agent to adopt a policy from a set of admissible policies. The goal of the reward designer is to modify the underlying reward function cost-efficiently while ensuring that any approximately optimal deterministic policy under the new reward function is admissible and performs well under the original reward function. This problem can be viewed as a dual to the problem of optimal reward poisoning attacks: instead of forcing an agent to adopt a specific policy, the reward designer incentivizes an agent to avoid taking actions that are inadmissible in certain states. Perhaps surprisingly, and in contrast to the problem of optimal reward poisoning attacks, we first show that the reward design problem for admissible policy teaching is computationally challenging, and it is NP-hard to find an approximately optimal reward modification. We then proceed by formulating a surrogate problem whose optimal solution approximates the optimal solution to the reward design problem in our setting, but is more amenable to optimization techniques and analysis. For this surrogate problem, we present characterization results that provide bounds on the value of the optimal solution. Finally, we design a local search algorithm to solve the surrogate problem and showcase its utility using simulation-based experiments.
Cross-Domain Collaborative Normalization via Structural Knowledge

Batch Normalization (BN) as an important component assists Deep Neural Networks in achieving promising performance for extensive learning tasks by scaling distribution of feature representations within mini-batches. However, the application of BN suffers from performance degradation under the scenario of Unsupervised Domain Adaptation (UDA), since the estimated statistics fail to concurrently describe two different domains. In this paper, we develop a novel normalization technique, named Collaborative Normalization (CoN), for eliminating domain discrepancy and accelerating the model training of neural networks for UDA. Unlike typical strategies only exploiting domain-specific statistics during normalization, our CoN excavates cross-domain knowledge and simultaneously scales features from various domains by mimicking the merits of collaborative representation. Our CoN can be easily plugged into popular neural network backbones for cross-domain learning. On the one hand, theoretical analysis guarantees that models with CoN promote discriminability of feature representations and accelerate convergence rate; on the other hand, empirical study verifies that replacing BN with CoN in popular network backbones effectively improves classification accuracy in most learning tasks across three cross-domain visual benchmarks.
Recovering the Propensity Score from Biased Positive Unlabeled Data

Positive-Unlabeled (PU) learning methods train a classifier to distinguish between the positive and negative classes given only positive and unlabeled data. While traditional PU methods require the labeled positive samples to be an unbiased sample of the positive distribution, in practice the labeled sample is often a biased draw from the true distribution. Prior work shows that if we know the likelihood that each positive instance will be selected for labeling, referred to as the propensity score, then the biased sample can be used for PU learning. Unfortunately, no prior work has been proposed an inference strategy for which the propensity score is identifiable. In this work, we propose two sets of assumptions under which the propensity score can be uniquely determined: one in which no assumption is made on the functional form of the propensity score (requiring assumptions on the data distribution), and the second which loosens the data assumptions while assuming a functional form for the propensity score. We then propose inference strategies for each case. Our empirical study shows that our approach significantly outperforms the state-of-the-art propensity estimation methods on a rich variety of benchmark datasets.
ADMM-Based Residual Whiteness Principle for Automatic Parameter Selection in Single Image Super-Resolution Problems

Abstract We propose an automatic parameter selection strategy for the single image super-resolution problem for images corrupted by blur and additive white Gaussian noise with unknown standard deviation. The proposed approach exploits the structure of both the down-sampling and the blur operators in the frequency domain and computes the optimal regularisation parameter as the one optimising a suitably defined residual whiteness measure. Computationally, the proposed strategy relies on the fast solution of generalised Tikhonov $$\ell _2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> – $$\ell _2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>ℓ</mml:mi> <mml:mn>2</mml:mn> </mml:msub> </mml:math> problems as proposed in Zhao et al. (IEEE Trans Image Process 25:3683–3697, 2016). These problems naturally appear as substeps of the Alternating Direction Method of Multipliers used to solve single image super-resolution problems with non-quadratic, non-smooth, sparsity-promoting regularisers both in convex and in non-convex regimes. After detailing the theoretical properties allowing to express the whiteness functional in a compact way, we report an exhaustive list of numerical experiments proving the effectiveness of the proposed approach for different type of problems, in comparison with well-known parameter selection strategies such as, e.g., the discrepancy principle.
A Study of Teaching Speaking Techniques Used at Peace English Course Pare, Kediri

This research aimed to find out what teaching speaking techniques are used and the reasons for using the techniques by the tutors at Peace English Course, Pare, Kediri to overcome learners’ difficulties in speaking. The method of this research was a descriptive qualitative method. The researchers obtained the data by observing the three classes of speaking programs, interviewing the tutors of the three classes, and data documents from the Peace English Course. The finding of this research showed that the techniques applied by the tutors were Storytelling, conversation, presenting, problem-solving, discussion, debate, and speech. Story-telling and conversation were applied for the beginner level class while presentation, problem-solving, discussion, debate, and speech were applied for the advanced level class. The reasons for using the techniques were because they were considered fun and were able to make the learners practice more. For the learners who have difficulties in learning to speak, the tutors encourage and motivate them to practice more because every difficulty in the speaking learning process is considered to be easily solved by having more practice.
Attendance Mobile Application With Face Recognition and Detect Location

Every company must have an attendance policy for each of its employees for the purpose of employee attendance and performance reports in the performance of their work, but currently there are still many companies that still have an absence system that is not computerized, so the calculation and accounting process is still done manually, because everything is calculated manually, it is possible that there are errors in the calculations and take time to process data absence of all employees. Therefore, an application that can automatically process and record data is needed to avoid errors and speed up the data processing process. In addition, the application can facilitate employees' attendance as it can be accessed directly from cell phones by using facial recognition and location detection to prove the attendance of employees.
Fairness analysis of extra-gain guilty of a non-repudiation protocol

Many traditional applications can be refined thanks to the development of blockchain technology. One of these services is non-repudiation, in which participants in a communication process cannot deny their involvement. Due to the vulnerabilities of the non-repudiation protocols, one of the parties involved in the communication can often avoid non-repudiation rules and obtain the expected information to the detriment of the interests of the other party, resulting in adverse effects. This paper studies the fairness guarantee quantitatively through probabilistic model checking. E-fairness is measured by modeling the protocol in probabilistic timed automata and verifying the appropriate property specified in the probabilistic computation tree logic. Furthermore, our analysis proposes insight for choosing suitable values for different parameters associated with the protocol so that a certain degree of fairness can be obtained. Therefore, the reverse question—for a certain degree of fairness ε, how can the protocol parameters be specified to ensure fairness—is answered.
How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning

Trial-to-trial effects have been found in a number of studies, indicating that processing a stimulus influences responses in subsequent trials. A special case are priming effects which have been modelled successfully with error-driven learning (Marsolek, 2008), implying that participants are continuously learning during experiments. This study investigates whether trial-to-trial learning can be detected in an unprimed lexical decision experiment. We used the Discriminative Lexicon Model (DLM; Baayen et al., 2019), a model of the mental lexicon with meaning representations from distributional semantics, which models error-driven incremental learning with the Widrow-Hoff rule. We used data from the British Lexicon Project (BLP; Keuleers et al., 2012) and simulated the lexical decision experiment with the DLM on a trial-by-trial basis for each subject individually. Then, reaction times were predicted with Generalised Additive Models (GAMs), using measures derived from the DLM simulations as predictors. We extracted measures from two simulations per subject (one with learning updates between trials and one without), and used them as input to two GAMs. Learning-based models showed better model fit than the non-learning ones for the majority of subjects. Our measures also provide insights into lexical processing and individual differences. This demonstrates the potential of the DLM to model behavioural data and leads to the conclusion that trial-to-trial learning can indeed be detected in unprimed lexical decision. Our results support the possibility that our lexical knowledge is subject to continuous changes.
Adaptive Global-Local Context Fusion for Multi-Turn Spoken Language Understanding

Recent years have seen significant advances in multi-turn Spoken Language Understanding (SLU), where dialogue contexts are used to guide intent classification and slot filling. However, how to selectively incorporate dialogue contexts, such as previous utterances and dialogue acts, into multi-turn SLU still remains a substantial challenge. In this work, we propose a novel contextual SLU model for multi-turn intent classification and slot filling tasks. We introduce an adaptive global-local context fusion mechanism to selectively integrate dialogue contexts into our model. The local context fusion aligns each dialogue context using multi-head attention, while the global context fusion measures overall context contribution to intent classification and slot filling tasks. Experiments show that on two benchmark datasets, our model achieves absolute F1 score improvements of 2.73% and 2.57% for the slot filling task on Sim-R and Sim M datasets, respectively. Additional experiments on a large-scale, de-identified, in-house dataset further verify the measurable accuracy gains of our proposed model.