Reviewerly/exHarmony huggingface dataset:
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain

Generative models are designed to address the data scarcity problem. Even with the exploding amount of data, due to computational advancements, some applications (e.g., health care, weather forecast, fault detection) still suffer from data insufficiency, especially in the time-series domain. Thus generative models are essential and powerful tools, but they still lack a consensual approach for quality assessment. Such deficiency hinders the confident application of modern implicit generative models on time-series data. Inspired by assessment methods on the image domain, we introduce the InceptionTime Score (ITS) and the Frechet InceptionTime Distance (FITD) to gauge the qualitative performance of class conditional generative models on the time-series domain. We conduct extensive experiments on 80 different datasets to study the discriminative capabilities of proposed metrics alongside two existing evaluation metrics: Train on Synthetic Test on Real (TSTR) and Train on Real Test on Synthetic (TRTS). Extensive evaluation reveals that the proposed assessment method, i.e., ITS and FITD in combination with TSTR, can accurately assess class-conditional generative model performance.
BERTScore is Unfair: On Social Bias in Language Model-Based Metrics for Text Generation

Automatic evaluation metrics are crucial to the development of generative systems. In recent years, pre-trained language model (PLM) based metrics, such as BERTScore, have been commonly adopted in various generation tasks. However, it has been demonstrated that PLMs encode a range of stereotypical societal biases, leading to a concern on the fairness of PLMs as metrics. To that end, this work presents the first systematic study on the social bias in PLM-based metrics. We demonstrate that popular PLM-based metrics exhibit significantly higher social bias than traditional metrics on 6 sensitive attributes, namely race, gender, religion, physical appearance, age, and socioeconomic status. In-depth analysis suggests that choosing paradigms (matching, regression, or generation) of the metric has a greater impact on fairness than choosing PLMs. In addition, we develop debiasing adapters that are injected into PLM layers, mitigating bias in PLM-based metrics while retaining high performance for evaluating text generation.
Training speech emotion classifier without categorical annotations

There are two paradigms of emotion representation, categorical labeling and dimensional description in continuous space. Therefore, the emotion recognition task can be treated as a classification or regression. The main aim of this study is to investigate the relation between these two representations and propose a classification pipeline that uses only dimensional annotation. The proposed approach contains a regressor model which is trained to predict a vector of continuous values in dimensional representation for given speech audio. The output of this model can be interpreted as an emotional category using a mapping algorithm. We investigated the performances of a combination of three feature extractors, three neural network architectures, and three mapping algorithms on two different corpora. Our study shows the advantages and limitations of the classification via regression approach.
Multi-Task Learning based Video Anomaly Detection with Attention

Multi-task learning based video anomaly detection methods combine multiple proxy tasks in different branches to detect video anomalies in different situations. Most existing methods either do not combine complementary tasks to effectively cover all motion patterns, or the class of the objects is not explicitly considered. To address the aforementioned shortcomings, we propose a novel multi-task learning based method that combines complementary proxy tasks to better consider the motion and appearance features. We combine the semantic segmentation and future frame prediction tasks in a single branch to learn the object class and consistent motion patterns, and to detect respective anomalies simultaneously. In the second branch, we added several attention mechanisms to detect motion anomalies with attention to object parts, the direction of motion, and the distance of the objects from the camera. Our qualitative results show that the proposed method considers the object class effectively and learns motion with attention to the aforementioned important factors which results in a precise motion modeling and a better motion anomaly detection. Additionally, quantitative results show the superiority of our method compared with state-of-the-art methods.
The Impact of Personality on Requirements Engineering Activities: A Mixed-Methods Study

Context: Requirements engineering (RE) is an important part of Software Engineering (SE), consisting of various human-centric activities that require the frequent collaboration of a variety of roles. Prior research has shown that personality is one such human aspect that has a huge impact on the success of a software project. However, a limited number of empirical studies exist focusing on the impact of personality on RE activities. Objective: The objective of this study is to explore and identify the impact of personality on RE activities, provide a better understanding of these impacts, and provide guidance on how to better handle these impacts in RE. Method: We used a mixed-methods approach, including a personality test-based survey (50 participants) and an in-depth interview study (15 participants) with software practitioners from around the world involved in RE activities. Results: Through personality test analysis, we found a majority of the practitioners have a high score on agreeableness and conscientiousness traits and an average score on extraversion and neuroticism traits. Through analysis of the interviews, we found a range of impacts related to the personality traits of software practitioners, their team members, and external stakeholders. These impacts can be positive or negative, depending on the RE activities, the overall software development process, and the people involved in these activities. Moreover, we found a set of strategies that can be applied to mitigate the negative impact of personality on RE activities. Conclusion: Our identified impacts of personality on RE activities and mitigation strategies serve to provide guidance to software practitioners on handling such possible personality impacts on RE activities and for researchers to investigate these impacts in greater depth in future.
EfficientVLM: Fast and Accurate Vision-Language Models via Knowledge Distillation and Modal-adaptive Pruning

Pre-trained vision-language models (VLMs) have achieved impressive results in a range of vision-language tasks. However, popular VLMs usually consist of hundreds of millions of parameters which brings challenges for fine-tuning and deployment in real-world applications due to space, memory, and latency constraints. In this work, we introduce a distilling then pruning framework to compress large vision-language models into smaller, faster, and more accurate ones. We first shrink the size of a pre-trained large VLM and apply knowledge distillation in the vision-language pre-training stage to obtain a task-agnostic compact VLM. Then we propose a modal-adaptive pruning algorithm to automatically infer the importance of vision and language modalities for different downstream tasks and adaptively remove redundant structures and neurons in different encoders with controllable target sparsity. We apply our framework to train EfficientVLM, a fast and accurate vision-language model consisting of 6 vision layers, 3 text layers, and 3 cross-modal fusion layers, accounting for only 93 million parameters in total, which is 44.3% of the teacher model. EfficientVLM retains 98.4% performance of the teacher model and accelerates its inference speed by 2.2x. EfficientVLM achieves a large absolute improvement over previous SoTA efficient VLMs of similar sizes by a large margin on various vision-language tasks, including VQAv2 (+4.9%), NLVR2 (+5.6%), ITR (R@1 on TR +17.2%, on IR + 15.6% ) and COCO caption generation (CIDEr +6.5), demonstrating a large potential on training lightweight VLMs.
Shadfa 0.1: The Iranian Movie Knowledge Graph and Graph-Embedding-Based Recommender System

Movies are a great source of entertainment. However, the problem arises when one is trying to find the desired content within this vast amount of data which is significantly increasing every year. Recommender systems can provide appropriate algorithms to solve this problem. The content_based technique has found popularity due to the lack of available user data in most cases. Content_based recommender systems are based on the similarity of items' demographic information; Term Frequency _ Inverse Document Frequency (TF_IDF) and Knowledge Graph Embedding (KGE) are two approaches used to vectorize data to calculate these similarities. In this paper, we propose a weighted content_based movie RS by combining TF_IDF which is an appropriate approach for embedding textual data such as plot/description, and KGE which is used to embed named entities such as the director's name. The weights between features are determined using a Genetic algorithm. Additionally, the Iranian movies dataset is created by scraping data from movie_related websites. This dataset and the structure of the FarsBase KG are used to create the MovieFarsBase KG which is a component in the implementation process of the proposed content_based RS. Using precision, recall, and F1 score metrics, this study shows that the proposed approach outperforms the conventional approach that uses TF_IDF for embedding all attributes.
Time inhomogeneous quantum dynamical maps

Abstract We discuss a wide class of time inhomogeneous quantum evolution which is represented by two-parameter family of completely positive trace-preserving maps. These dynamical maps are constructed as infinite series of jump processes. It is shown that such dynamical maps satisfy time inhomogeneous memory kernel master equation which provides a generalization of the master equation involving the standard convolution. Time-local (time convolution-less) approach is discussed as well. Finally, the comparative analysis of traditional time homogeneous vs. time inhomogeneous scenario is provided.
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor Attacks

Natural language processing (NLP) models are known to be vulnerable to backdoor attacks, which poses a newly arisen threat to NLP models. Prior online backdoor defense methods for NLP models only focus on the anomalies at either the input or output level, still suffering from fragility to adaptive attacks and high computational cost. In this work, we take the first step to investigate the unconcealment of textual poisoned samples at the intermediate-feature level and propose a feature-based efficient online defense method. Through extensive experiments on existing attacking methods, we find that the poisoned samples are far away from clean samples in the intermediate feature space of a poisoned NLP model. Motivated by this observation, we devise a distance-based anomaly score (DAN) to distinguish poisoned samples from clean samples at the feature level. Experiments on sentiment analysis and offense detection tasks demonstrate the superiority of DAN, as it substantially surpasses existing online defense methods in terms of defending performance and enjoys lower inference costs. Moreover, we show that DAN is also resistant to adaptive attacks based on feature-level regularization. Our code is available at https://github.com/lancopku/DAN.
Efficiently Controlling Multiple Risks with Pareto Testing

Machine learning applications frequently come with multiple diverse objectives and constraints that can change over time. Accordingly, trained models can be tuned with sets of hyper-parameters that affect their predictive behavior (e.g., their run-time efficiency versus error rate). As the number of constraints and hyper-parameter dimensions grow, naively selected settings may lead to sub-optimal and/or unreliable results. We develop an efficient method for calibrating models such that their predictions provably satisfy multiple explicit and simultaneous statistical guarantees (e.g., upper-bounded error rates), while also optimizing any number of additional, unconstrained objectives (e.g., total run-time cost). Building on recent results in distribution-free, finite-sample risk control for general losses, we propose Pareto Testing: a two-stage process which combines multi-objective optimization with multiple hypothesis testing. The optimization stage constructs a set of promising combinations on the Pareto frontier. We then apply statistical testing to this frontier only to identify configurations that have (i) high utility with respect to our objectives, and (ii) guaranteed risk levels with respect to our constraints, with specifiable high probability. We demonstrate the effectiveness of our approach to reliably accelerate the execution of large-scale Transformer models in natural language processing (NLP) applications. In particular, we show how Pareto Testing can be used to dynamically configure multiple inter-dependent model attributes -- including the number of layers computed before exiting, number of attention heads pruned, or number of text tokens considered -- to simultaneously control and optimize various accuracy and cost metrics.
HACNet: End-to-end learning of table-to-image converter and convolutional neural network

Abstract Motivated by the high prediction performance of convolutional neural networks (CNNs), several works have applied them to tabular datasets. As CNNs are built to accept images, several transformations of tabular data have been proposed to obtain images. However, existing methods transform the tabular data to images prior to CNN training, which fails to take the prediction error into account. Additionally, they employ all features from the tables, including unimportant ones, to produce the images. Moreover, the created images might not become human-interpretable because they do not consider the interpretability of images as a metric. To overcome these problems, we propose a hard attention-based converter combined with a convolutional neural network (HACNet), consisting of an attention-based table-to-image converter and a CNN-based predictor. HACNet trains its components simultaneously by minimizing CNN prediction loss and mean squared error (MSE) between created and template images. Minimizing this MSE allows us to visually distinguish the created images with different labels. The attention-based converter selects exactly one feature for each pixel in the image via its hard-attention mechanism with Gumbel-Softmax, enabling feature selection. We experimentally show that HACNet produces human-interpretable images, reduces used features, and achieves prediction performances comparative with existing methods on several benchmark datasets.
Modified DRTA (Directed Reading Thinking Activity) Strategy to Enhance Students' Reading Comprehension

The objective of this research was to boost students' reading comprehension of narrative text by modifying DRTA (Directed Reading Thinking Activity) strategy at X MIPA 3 students of SMA Negeri 1 Bintan Timur. The research design was classroom action research. This study held at two cycles with four meetings in each cycle. The subjects of this research were X MIPA 3 students which made up 36 students. The research instruments applied to gather the data were observation checklist and reading comprehension test. The result showed that students' percentage whose got score ≥ 75 in cycle 1 and cycle 2 were higher than preliminary study. There were 63,88% students in cycle 1 and 88,88% students in cycle 2. It showed that the students' percentage whose got score ≥ 75 improved and meet the criterion of success was 70%. The observation checklist's results also showed that modified DRTA (Directed Reading Thinking Activity) strategy was successful in enhanciing students' classroom participation. The students' participation had been reached 83% in cycle 2. From the result, ≥ 40% students participated in the teaching and learning activities when the strategy was implemented. It could be concluded that the modified DRTA (Directed Reading Thinking Activity) strategy could improve students' reading comprehension and students' participation at X MIPA 3 students of SMA Negeri 1 Bintan Timur.
In, During, The, At, After Improving Students' Speaking Skills by Conducting Small Group Discussions in Grade 8 Smpn 14 Bintan

This study aimed to describe how small group discussions improved students' speaking skills in class VIII of SMPN 14 Bintan. The method used in this research is classroom action research (CAR). The population in this study were all class VIII C of SMPN 14 Bintan students in the 2021/2022 academic year, consisting of 28 students. The data was collected through pre-test and post-test. In the first test (pre-test), the number of students who met the minimum completeness criteria of 75 was 1 out of 28 students (5%). In the second test (post-test I), students who scored above 75 or met the minimum completeness criteria were 7 out of 28 students (25%). In the third test (post-test II), 16 of 28 students (57.14%) met the minimum completeness criteria of 75. The result shows that there are some improvements in the students' speaking skills quantitatively and qualitatively. Moreover, students were more active and participated in the teaching and learning process of speaking. To conclude, the SGD worked excellently and efficiently in helping students speak English in the eighth grade of SMPN 14 Bintan.
Developing A Student Handout of English Material for Grade Eight

This study aimed to develop a student handout as an English material for eighth grade and to know the validity and practicality of it. A handout was one form of teaching material that could be used and could support the learning process. This study was a research and development. The model of research and development used was the 4D development model proposed by Thiagarajan which has been modified into 3D by Wati (2012). 3D model only consists of three main steps, that are Define, Design, and Development. The results of the study showed that the student handout has been developed by following the steps of the development model which was used. Through validation by two material expert validators, the validity result of the initial design of student handout has obtained the average score percentage from validator 1 was 84.72% with very valid category and from validator 2 was 75% with valid category. In a limited trial, the practicality result of the student handout reached to average score percentage of students’ responses was 82.24% with very practical category, so that the final product of student handout is obtained.
A Rewriting Logic Approach to Specification, Proof-search, and Meta-proofs in Sequent Systems

This paper develops an algorithmic-based approach for proving inductive properties of propositional sequent systems such as admissibility, invertibility, cut-elimination, and identity expansion. Although undecidable in general, these structural properties are crucial in proof theory because they can reduce the proof-search effort and further be used as scaffolding for obtaining other meta-results such as consistency. The algorithms -- which take advantage of the rewriting logic meta-logical framework, and use rewrite- and narrowing-based reasoning -- are explained in detail and illustrated with examples throughout the paper. They have been fully mechanized in the L-Framework, thus offering both a formal specification language and off-the-shelf mechanization of the proof-search algorithms coming together with semi-decision procedures for proving theorems and meta-theorems of the object system. As illustrated with case studies in the paper, the L-Framework, achieves a great degree of automation when used on several propositional sequent systems, including single conclusion and multi-conclusion intuitionistic logic, classical logic, classical linear logic and its dyadic system, intuitionistic linear logic, and normal modal logics.
Agricultural Financing and Economic Growth in Nigeria

The study examined the influence of agricultural financing on economic growth in Nigeria over the period 1981 to 2019. The objective of the study is to the impact of the long-term relationship between various forms of agricultural financing on Nigeria’s economic growth. The study employs the stationary test, the co-integration test, the error correction model and the Granger causality model. All variables were stationary at the first difference, and the co-integration test evidenced a long-run relationship. The study identified, in the long run, that; the agricultural credit guarantee scheme fund shows a positive and significant influence on the gross domestic product in Nigeria. The commercial bank loan and community –micro-finance bank loan shows a positive and significant influence on the gross domestic product in Nigeria within the reference period. In light of the findings, the study concluded that all the variables employed predict Nigeria’s gross domestic product. Furthermore, the granger causality results show a demand following a role as against supply leading role, revealing that increase in output level in Nigeria significantly support/promotes agricultural financial instrument. The study recommends that the Federal government should motivate commercial banks to provide adequate credit facilities to the Agricultural sector through moderate bank lending rates for ease of farming business in Nigeria. Proactive campaigns on the availability of credit facilities for farmers to enable them to have access to loans at a single-digit interest rate. This could be achieved through social media, door-to-door vibes, town hall meetings and market squares. Government should create an enabling environment for farmers through the provision of adequate security, pest control measures and seedlings.
Markovian Quantum Antenna Theory

&lt;p&gt;We describe a general framework for the modeling and analysis of Markovian quantum antenna systems viewed as a special problem in quantum stochastic dynamics. The quantum radiator, which can be used to spatially direct radiated quantum states in future quantum communication systems, is modeled as an open quantum system capable of controlling the composition of its radiation modes through external source manipulations while in continuous interaction with the surrounding thermal and random environment. Our analysis and computational method are based on deploying the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) master equation to account for environment-induced jump processes such as quantum dissipation and decoherence. We construct a definition of the quantum antenna directivity inspired by the corresponding formulas in classical antenna theory and use the GKSL formalism to derive several versions of the quantum directivity formula. As a computational example, we study the flow of the density operator of a coupled two-level quantum dot (qubit) array, excited by classical external signals with variable amplitude and phase, which is evolved in time using the quantum Liouville-type equation (the master equation). It is shown that by manipulating the amplitude and phase excitations of individual quantum dots, one may significantly enhance the directive radiation properties of the Markovian quantum antenna system.&lt;/p&gt;