Reviewerly/exHarmony huggingface dataset:
Ambiguity in iXML

Humans are really good at resolving ambiguities. Our senses are trained for it: is that pattern of shadows in the forest dappled sunlight, or a tiger waiting to pounce? Our minds quickly and almost effortlessly adjust interpretations based on contextual clues that change over time. Parsers? Not so much. Our everyday languages and formats: XML, JSON, JavaScript, Java, etc. are rigorously defined to avoid ambiguity: you must put a quote here, a semicolon there. (Most) parsers reject anything that cannot be unambiguously identified within a small textual window. Invisible XML is an uncommon format in that it doesn’t reject grammars or parses that are ambiguous. That doesn’t mean ambiguity is a good thing, and it doesn’t mean authors wouldn’t like to control it.
Study of Topological Behavior of Some Computer Related Graphs

Network theory is the study of graphs such as representing equilibrium relationships or unequal relationships between different objects. A network can be defined as a graph where nodes and / or margins have attributes (e.g. words). Topological index of a graph is a number that helps to understand its topology and a topological index is known as irregularity index if it is greater than zero and topological index of graph is equal to zero if and only if graph is regular. The irregularity indices are used for computational analysis of nonregular graph topological composition. In this paper, we aim to compute topological invariants of some computer related graph networks. We computed various irregularities indices for the graphs of OTIS swapped network OPa and Biswapped Networks Bsw(Pa).
Benchmarking of different optimizers in the variational quantum algorithms for applications in quantum chemistry

Classical optimizers play a crucial role in determining the accuracy and convergence of variational quantum algorithms; leading algorithms use a near-term quantum computer to solve the ground state properties of molecules, simulate dynamics of different quantum systems, and so on. In the literature, many optimizers, each having its own architecture, have been employed expediently for different applications. In this work, we consider a few popular and efficacious optimizers and assess their performance in variational quantum algorithms for applications in quantum chemistry in a realistic noisy setting. We benchmark the optimizers with critical analysis based on quantum simulations of simple molecules, such as hydrogen, lithium hydride, beryllium hydride, water, and hydrogen fluoride. The errors in the ground state energy, dissociation energy, and dipole moment are the parameters used as yardsticks. All the simulations were carried out with an ideal quantum circuit simulator, a noisy quantum circuit simulator, and finally a noisy simulator with noise embedded from the IBM Cairo quantum device to understand the performance of the classical optimizers in ideal and realistic quantum environments. We used the standard unitary coupled cluster ansatz for simulations, and the number of qubits varied from two starting from the hydrogen molecule to ten qubits in hydrogen fluoride. Based on the performance of these optimizers in the ideal quantum circuits, the conjugate gradient, limited-memory Broyden—Fletcher—Goldfarb—Shanno bound, and sequential least squares programming optimizers are found to be the best-performing gradient-based optimizers. While constrained optimization by linear approximation (COBYLA) and Powell's conjugate direction algorithm for unconstrained optimization (POWELL) perform most efficiently among the gradient-free methods, in noisy quantum circuit conditions, simultaneous perturbation stochastic approximation, POWELL, and COBYLA are among the best-performing optimizers.
照度センサを用いた在宅時の活動見守りシステム

A social issue that leads to the safety and security of people is the network that monitors the safety of people living alone in remote areas. Various research and developments of indoor human activity recognition using IoT sensor nodes have been conducted in recent years. In this research, we focus on turning on and off indoor lighting and propose an activity recognition system at home using an illuminance sensor. An IoT sensor node with a built-in illuminance sensor is attached to the wall near the indoor lighting equipment. The living activities of people in the room are estimated from changes in the illuminance. We assume a significant change in the illuminance of the indoor lighting is observed at least once a day at the sampling interval of the IoT sensor node. In that case, it is possible to estimate the operation of the indoor lighting due to daily activities and to confirm the safety of people living alone in remote areas. The activity recognition system was evaluated for a total of 380 days, excluding the missing period. The estimation of indoor lighting manipulation by daily activities was accurate for 376 days. Precision, Recall, and F-Measure score values, which are evaluation indices for activity estimation, were 94.9%, 98.2%, and 96.5%, respectively.
Authentication

Abstract An authentication process is a process of verifying an entity’s identity based on one or multiple factors. A factor can be something the entity is, possesses, or knows. Depending on the number of credentials (or factors) required, the authentication process is referred to as single-factor authentication, two-factor authentication, or multifactor authentication. In choosing a particular authentication solution for organizations, it is crucial to balance security, usability, cost, and privacy considerations. The authentication solution for a particular service can be predetermined for the private individual, although stronger authentication can be enabled if desired. While these trends suggest a more secure and user-friendly authentication process, they may also introduce new privacy concerns that must be addressed.
Opt-RNN-DBFSVM: Optimal recurrent neural network density based fuzzy support vector machine

Two major problems are encountered when using fuzzy SVM: (a) the number of local minima increases exponentially with the number of samples and (b) the quantity of required computer storage, required for a regular quadratic programming solver, increases by an exponential magnitude as the problem size expands. The Kernel-Adatron family of algorithms gaining attention lately which has allowed to handle very large classification and regression problems. However, these methods treat different types of samples (Noise, border, and core) with the same manner, which causes searches in unpromising areas and increases the number of iterations. In this work, we introduce a hybrid method to overcome these shortcoming, namely Optimal Recurrent Neural Network Density Based fuzzy Support Vector Machine (Opt-RNN-DBFSVM). This method consists of four steps: (a) characterization of different samples, (b) elimination of samples with a low probability of being a support vector, (c) construction of an appropriate recurrent neural network based on an original energy function, and (d) solution of the system of differential equations, managing the dynamics of the RNN, using the Euler–Cauchy method involving an optimal time step. Thanks to its recurrent architecture, the RNN remembers the regions explored during the search process. We demonstrated that RNN-FSVM converges to feasible support vectors and Opt-RNN-DBFSVM has a very low time complexity compared to RNN-FSVM with constant time step, and KAs-FSVM. Several experiments were performed on academic data sets. We used several classification performance measures to compare Opt-RNN-DBFSVM to different classification methods and the results obtained show the good performance of the proposed method.
Optimizing Homomorphic Evaluation Circuits by Program Synthesis and Time-bounded Exhaustive Search

We present a new and general method for optimizing homomorphic evaluation circuits. Although fully homomorphic encryption (FHE) holds the promise of enabling safe and secure third party computation, building FHE applications has been challenging due to their high computational costs. Domain-specific optimizations require a great deal of expertise on the underlying FHE schemes and FHE compilers that aim to lower the hurdle, generate outcomes that are typically sub-optimal, as they rely on manually-developed optimization rules. In this article, based on the prior work of FHE compilers, we propose a method for automatically learning and using optimization rules for FHE circuits. Our method focuses on reducing the maximum multiplicative depth, the decisive performance bottleneck, of FHE circuits by combining program synthesis, term rewriting, and equality saturation. It first uses program synthesis to learn equivalences of small circuits as rewrite rules from a set of training circuits. Then, we perform term rewriting on the input circuit to obtain a new circuit that has lower multiplicative depth. Our rewriting method uses the equational matching with generalized version of the learned rules, and its soundness property is formally proven. Our optimizations also try to explore every possible alternative order of applying rewrite rules by time-bounded exhaustive search technique called equality saturation. Experimental results show that our method generates circuits that can be homomorphically evaluated 1.08×–3.17× faster (with the geometric mean of 1.56×) than the state-of-the-art method. Our method is also orthogonal to existing domain-specific optimizations.
Extracurricular projects ‐ Teaching Systems architecting in a limited time‐span

University student projects experience high turnover, resulting in a loss of knowledge and experience. Turnover leads to increased project risk, development delays, and loss of institutional knowledge. These issues are particularly relevant for systems engineering teams as the discipline requires a broad range of competencies and expertise. This paper presents an experience report of a case study with results that suggest that it is possible to provide undergraduate students with a foundational body of SysEng knowledge using mission analysis as a learning platform. This activity can be completed within one semester and allows the undergrad students to continue learning independently.
Alternating Joint Learning Approach for Variational Networks and Sampling Pattern in Parallel MRI

We propose a new alternating learning approach to jointly learn the sampling pattern (SP) and the parameters of a variational network (VN) for acquisition and reconstruction on 3D Cartesian parallel MRI problems. This approach is composed of alternating short training with BASS algorithm to learn the SP, and ADAM algorithm to learn the parameters of the VN, both with forced monotonicity. The results illustrate that this approach provides reduced error when compared to other joint learning approaches, and surpasses VN trained with recently developed fixed SPs.
Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation

An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems. To achieve this, there are four properties that a watch time prediction framework should satisfy: first, despite its continuous value, watch time is also an ordinal variable and the relative ordering between its values reflects the differences in user preferences. Therefore the ordinal relations should be reflected in watch time predictions. Second, the conditional dependence between the video-watching behaviors should be captured in the model. For instance, one has to watch half of the video before he/she finishes watching the whole video. Third, modeling watch time with a point estimation ignores the fact that models might give results with high uncertainty and this could cause bad cases in recommender systems. Therefore the framework should be aware of prediction uncertainty. Forth, the real-life recommender systems suffer from severe bias amplifications thus an estimation without bias amplification is expected.
Meta Graph Learning for Long-tail Recommendation

Highly skewed long-tail item distribution commonly hurts model performance on tail items in recommendation systems, especially for graph-based recommendation models. We propose a novel idea to learn relations among items as an auxiliary graph to enhance the graph-based representation learning and make recommendations collectively in a coupled framework. This raises two challenges, 1) the long-tail downstream information may also bias the auxiliary graph learning, and 2) the learned auxiliary graph may cause negative transfer to the original user-item bipartite graph. We innovatively propose a novel Meta Graph Learning framework for long-tail recommendation (MGL) for solving both challenges. The meta-learning strategy is introduced to the learning of an edge generator, which is first tuned to reconstruct a debiased item co-occurrence matrix, and then virtually evaluated on generating item relations for recommendation. Moreover, we propose a popularity-aware contrastive learning strategy to prevent negative transfer by aligning the confident head item representations with those of the learned auxiliary graph. Experiments on public datasets demonstrate that our proposed model significantly outperforms strong baselines for tail items without compromising the overall performance.
KoSBI: A Dataset for Mitigating Social Bias Risks Towards Safer Large Language Model Applications

Large language models (LLMs) learn not only natural text generation abilities but also social biases against different demographic groups from real-world data.This poses a critical risk when deploying LLM-based applications.Existing research and resources are not readily applicable in South Korea due to the differences in language and culture, both of which significantly affect the biases and targeted demographic groups.This limitation requires localized social bias datasets to ensure the safe and effective deployment of LLMs.To this end, we present KOSBI, a new social bias dataset of 34k pairs of contexts and sentences in Korean covering 72 demographic groups in 15 categories.We find that through filtering-based moderation, social biases in generated content can be reduced by 16.47%p on average for HyperCLOVA (30B and 82B), and GPT-3.
Counterfactual Video Recommendation for Duration Debiasing

Duration bias widely exists in video recommendations, where models tend to recommend short videos for the higher ratio of finish playing and thus possibly fail to capture users' true interests. In this paper, we eliminate the duration bias from both data and model. First, based on the extensive data analysis, we observe that play completion rate of videos with the same duration presents a bimodal distribution. Hence, we propose to perform threshold division to construct binary labels as training labels for alleviating the drawback of finish playing labels overly biased towards short videos. Algorithmically, we resort to causal inference, which enables us to inspect causal relationships of video recommendations with a causal graph. We identify that duration has two kinds of effect on prediction: direct and indirect. Duration bias lies in the direct effect, while the indirect effect benefits prediction. To this end, we design a model-agnostic Counterfactual Video Recommendation for Duration Debiasing (CVRDD) framework, which incorporates multi-task learning to estimate different causal effect during training. In the inference phase, we perform counterfactual inference to remove the direct effect of duration for unbiased prediction. We conduct experiments on two industrial datasets, and in addition to achieving highly promising results on traditional top-k recommendation metrics, CVRDD also improves the user watch time.
AraBEM at WANLP 2022 Shared Task: Propaganda Detection in Arabic Tweets

Propaganda is information or ideas that an organised group or government spreads to influence peopleś opinions, especially by not giving all the facts or secretly emphasising only one way of looking at the points.The ability to automatically detect propaganda-related language is a challenging task that researchers in the NLP community have recently started to address.This paper presents the participation of our team AraBEM in the propaganda detection shared task on Arabic tweets.Our system utilised a pre-trained BERT model to perform multi-class binary classification.It attained the best score at 0.602 micro-f1, ranking third on subtask-1, which identifies the propaganda techniques as a multilabel classification problem with a baseline of 0.079.
DisasterNet: Causal Bayesian Networks with Normalizing Flows for Cascading Hazards Estimation from Satellite Imagery

Sudden-onset hazards like earthquakes often induce cascading secondary hazards (e.g., landslides, liquefaction, debris flows, etc.) and subsequent impacts (e.g., building and infrastructure damage) that cause catastrophic human and economic losses. Rapid and accurate estimates of these hazards and impacts are critical for timely and effective post-disaster responses. Emerging remote sensing techniques provide pre- and post-event satellite images for rapid hazard estimation. However, hazards and damage often co-occur or colocate with underlying complex cascading geophysical processes, making it challenging to directly differentiate multiple hazards and impacts from satellite imagery using existing single-hazard models. We introduce DisasterNet, a novel family of causal Bayesian networks to model processes that a major hazard triggers cascading hazards and impacts and further jointly induces signal changes in remotely sensed observations. We integrate normalizing flows to effectively model the highly complex causal dependencies in this cascading process. A triplet loss is further designed to leverage prior geophysical knowledge to enhance the identifiability of our highly expressive Bayesian networks. Moreover, a novel stochastic variational inference with normalizing flows is derived to jointly approximate posteriors of multiple unobserved hazards and impacts from noisy remote sensing observations. Integrating with the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system, our framework is evaluated in recent global earthquake events. Evaluation results show that DisasterNet significantly improves multiple hazard and impact estimation compared to existing USGS products.
Theoretical Convergence Guaranteed Resource-Adaptive Federated Learning with Mixed Heterogeneity

In this paper, we propose an adaptive learning paradigm for resource-constrained cross-device federated learning, in which heterogeneous local submodels with varying resources can be jointly trained to produce a global model. Different from existing studies, the submodel structures of different clients are formed by arbitrarily assigned neurons according to their local resources. Along this line, we first design a general resource-adaptive federated learning algorithm, namely RA-Fed, and rigorously prove its convergence with asymptotically optimal rate O(1/√Γ*TQ) under loose assumptions. Furthermore, to address both submodels heterogeneity and data heterogeneity challenges under non-uniform training, we come up with a new server aggregation mechanism RAM-Fed with the same theoretically proved convergence rate. Moreover, we shed light on several key factors impacting convergence, such as minimum coverage rate, data heterogeneity level, submodel induced noises. Finally, we conduct extensive experiments on two types of tasks with three widely used datasets under different experimental settings. Compared with the state-of-the-arts, our methods improve the accuracy up to 10% on average. Particularly, when submodels jointly train with 50% parameters, RAM-Fed achieves comparable accuracy to FedAvg trained with the full model.
GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation

We present GLM-Dialog, a large-scale language model (LLM) with 10B parameters capable of knowledge-grounded conversation in Chinese using a search engine to access the Internet knowledge. GLM-Dialog offers a series of applicable techniques for exploiting various external knowledge including both helpful and noisy knowledge, enabling the creation of robust knowledge-grounded dialogue LLMs with limited proper datasets. To evaluate the GLM-Dialog more fairly, we also propose a novel evaluation method to allow humans to converse with multiple deployed bots simultaneously and compare their performance implicitly instead of explicitly rating using multidimensional metrics. Comprehensive evaluations from automatic to human perspective demonstrate the advantages of GLM-Dialog comparing with existing open source Chinese dialogue models. We release both the model checkpoint and source code, and also deploy it as a WeChat application to interact with users. We offer our evaluation platform online in an effort to prompt the development of open source models and reliable dialogue evaluation systems. All the source code is available on Github.
Improving Conversational Recommendation Systems via Counterfactual Data Simulation

Conversational recommender systems~(CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data.
Word sense extension

Humans often make creative use of words to express novel senses.A long-standing effort in natural language processing has been focusing on word sense disambiguation (WSD), but little has been explored about how the sense inventory of a word may be extended toward novel meanings.We present a paradigm of word sense extension (WSE) that enables words to spawn new senses toward novel context.We develop a framework that simulates novel word sense extension by first partitioning a polysemous word type into two pseudo-tokens that mark its different senses, and then inferring whether the meaning of a pseudo-token can be extended to convey the sense denoted by the token partitioned from the same word type.Our framework combines cognitive models of chaining with a learning scheme that transforms a language model embedding space to support various types of word sense extension.We evaluate our framework against several competitive baselines and show that it is superior in predicting plausible novel senses for over 7,500 English words.Furthermore, we show that our WSE framework improves performance over a range of transformer-based WSD models in predicting rare word senses with few or zero mentions in the training data.
Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge

In empathetic conversations, individuals express their empathy towards others.Previous work has mainly focused on generating empathetic responses by utilizing the speaker's emotion.Besides, external commonsense knowledge has been applied to enhance the system's understandings of the speaker's situation.However, given an event, commonsense knowledge base contains various relations, potentially leading to confusion for the dialogue system.Consequently, inconsistencies arise among the emotion, generated response and speaker's contextual information.To this end, we propose a novel approach for empathetic response generation, which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker's situation.This selected knowledge is used to refine the commonsense cognition and empathy expression for generated responses.Experimental results show that our approach significantly outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses.Moreover, case studies highlight the interpretability of knowledge selection in the responses and the effectiveness of adaptive module in our model.