Reviewerly/exHarmony huggingface dataset:
The Functional Machine Calculus II: Semantics

The Functional Machine Calculus (FMC), recently introduced by the authors, is a generalization of the lambda-calculus which may faithfully encode the effects of higher-order mutable store, I/O and probabilistic/non-deterministic input. Significantly, it remains confluent and can be simply typed in the presence of these effects. In this paper, we explore the denotational semantics of the FMC. We have three main contributions: first, we argue that its syntax -- in which both effects and lambda-calculus are realised using the same syntactic constructs -- is semantically natural, corresponding closely to the structure of a Scott-style domain theoretic semantics. Second, we show that simple types confer strong normalization by extending Gandy's proof for the lambda-calculus, including a small simplification of the technique. Finally, we show that the typed FMC (without considering the specifics of encoded effects), modulo an appropriate equational theory, is a complete language for Cartesian closed categories.
Pruned Lightweight Encoders for Computer Vision

Latency-critical computer vision systems, such as autonomous driving or drone control, require fast image or video compression when offloading neural network inference to a remote computer. To ensure low latency on a near-sensor edge device, we propose the use of lightweight encoders with constant bitrate and pruned encoding configurations, namely, ASTC and JPEG XS. Pruning introduces significant distortion which we show can be recovered by retraining the neural network with compressed data after decompression. Such an approach does not modify the network architecture or require coding format modifications. By retraining with compressed datasets, we reduced the classification accuracy and segmentation mean intersection over union (mIoU) degradation due to ASTC compression to 4.9-5.0 percentage points (pp) and 4.4-4.0 pp, respectively. With the same method, the mIoU lost due to JPEG XS compression at the main profile was restored to 2.7-2.3 pp. In terms of encoding speed, our ASTC encoder implementation is 2.3x faster than JPEG. Even though the JPEG XS reference encoder requires optimizations to reach low latency, we showed that disabling significance flag coding saves 22–23% of encoding time at the cost of 0.4-0.3 mIoU after retraining.
DeepVulSeeker: A Novel Vulnerability Identification Framework via Code Graph Structure and Pre-training Mechanism

Software vulnerabilities can pose severe harms to a computing system. They can lead to system crash, privacy leakage, or even physical damage. Correctly identifying vulnerabilities among enormous software codes in a timely manner is so far the essential prerequisite to patch them. Unfortantely, the current vulnerability identification methods, either the classic ones or the deep-learning-based ones, have several critical drawbacks, making them unable to meet the present-day demands put forward by the software industry. To overcome the drawbacks, in this paper, we propose DeepVulSeeker, a novel fully automated vulnerability identification framework, which leverages both code graph structures and the semantic features with the help of the recently advanced Graph Representation Self-Attention and pre-training mechanisms. Our experiments show that DeepVulSeeker not only reaches an accuracy as high as 0.99 on traditional CWE datasets, but also outperforms all other exisiting methods on two highly-complicated datasets. We also testified DeepVulSeeker based on three case studies, and found that DeepVulSeeker is able to understand the implications of the vulnerbilities. We have fully implemented DeepVulSeeker and open-sourced it for future follow-up research.
Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data

Non-independent and identically distributed (non-IID) data is a key challenge in federated learning (FL), which usually hampers the optimization convergence and the performance of FL. Existing data augmentation methods based on federated generative models or raw data sharing strategies for solving the non-IID problem still suffer from low performance, privacy protection concerns, and high communication overhead in decentralized tabular data. To tackle these challenges, we propose a federated tabular data augmentation method, named Fed-TDA. The core idea of Fed-TDA is to synthesize tabular data for data augmentation using some simple statistics (e.g., distributions of each column and global covariance). Specifically, we propose the multimodal distribution transformation and inverse cumulative distribution mapping respectively synthesize continuous and discrete columns in tabular data from a noise according to the pre-learned statistics. Furthermore, we theoretically analyze that our Fed-TDA not only preserves data privacy but also maintains the distribution of the original data and the correlation between columns. Through extensive experiments on five real-world tabular datasets, we demonstrate the superiority of Fed-TDA over the state-of-the-art in test performance and communication efficiency.
Average Token Delay: A Latency Metric for Simultaneous Translation

Simultaneous translation is a task in which translation begins before the speaker has finished speaking. In its evaluation, we have to consider the latency of the translation in addition to the quality. The latency is preferably as small as possible for users to comprehend what the speaker says with a small delay. Existing latency metrics focus on when the translation starts but do not consider adequately when the translation ends. This means such metrics do not penalize the latency caused by a long translation output, which actually delays users' comprehension. In this work, we propose a novel latency evaluation metric called Average Token Delay (ATD) that focuses on the end timings of partial translations in simultaneous translation. We discuss the advantage of ATD using simulated examples and also investigate the differences between ATD and Average Lagging with simultaneous translation experiments.
Evolutionary Generalized Zero-Shot Learning

An open problem on the path to artificial intelligence is generalization from the known to the unknown, which is instantiated as Generalized Zero-Shot Learning (GZSL) task. In this work, we propose a novel Evolutionary Generalized Zero-Shot Learning setting, which (i) avoids the domain shift problem in inductive GZSL, and (ii) is more in line with the needs of real-world deployments than transductive GZSL. In the proposed setting, a zero-shot model with poor initial performance is able to achieve online evolution during application. We elaborate on three challenges of this special task, i.e., catastrophic forgetting, initial prediction bias, and evolutionary data class bias. Moreover, we propose targeted solutions for each challenge, resulting in a generic method capable of continuing to evolve on a given initial IGZSL model. Experiments on three popular GZSL benchmark datasets show that our model can learn from the test data stream while other baselines fail.
Privacy-Preserving Application-to-Application Authentication Using Dynamic Runtime Behaviors

Application authentication is typically performed using some form of secret credentials such as cryptographic keys, passwords, or API keys. Since clients are responsible for securely storing and managing the keys, this approach is vulnerable to attacks on clients. Similarly a centrally managed key store is also susceptible to various attacks and if compromised, can leak credentials. To resolve such issues, we propose an application authentication, where we rely on unique and distinguishable application's behavior to lock the key during a setup phase and unlock it for authentication. Our system add a fuzzy-extractor layer on top of current credential authentication systems. During a key enrollment process, the application's behavioral data collected from various sensors in the network are used to hide the credential key. The fuzzy extractor releases the key to the server if the application's behavior during the authentication matches the one collected during the enrollment, with some noise tolerance. We designed the system, analyzed its security, and implemented and evaluated it using 10 real-life applications deployed in our network. Our security analysis shows that the system is secure against client compromise, vault compromise, and feature observation. The evaluation shows the scheme can achieve 0 percent False Accept Rate with an average False Rejection Rate 14 percent and takes about 51 ms to successfully authenticate a client. In light of these promising results, we expect our system to be of practical use, since its deployment requires zero to minimal changes on the server.
Results of Competence Based Training in the Modules Related to "Algorithms and Data Structures" in the Profession of "Application Programmer"

The article describes a pedagogical research, conducted to determine the benefits of Competence Based Training (CBT) as part of a more general paradigm for Competence based education (CBE), in the modules related to Algorithms and data structures (ADS) in the profession of "Application programmer (occupation "Application programmer").For the purposes of the study, CBT was conducted in several groups under the National Program "IT Career Training" (NPITC).A comparison was made with groups from the regular form of training in the same profession, where a traditional approach to education is applied.The results of the study show that the implementation of CBE under the National Program "IT Career Training" provides no worse training than the traditional approach used in vocational schools.Even in the study area of Algorithms and data structures, the experimental group showed significantly better results than the control group, which means achieving a higher level of acquiring knowledge and skills, as well as more successful formation of specific competencies to work with ADS and application of the paradigms FPS (Functional programming style) and OOP (Objectoriented programming).
Research on Using Semantic Web Technologies in Content Management Systems

In the present paper, the usage of Semantic web technologies for the purposes of Content Management Systems (CMSs) is explored. A review of the research related to semantic CMS is performed. The distinguishing features between CMSs that support Semantic web technologies and native semantic CMSs are discussed. The possibilities of semantic CMSs to overcome the challenges posed by big data are considered.
Feature Purification: How Adversarial Training Performs Robust Deep Learning

Despite the empirical success of using Adversarial Training to defend deep learning models against adversarial perturbations, so far, it still remains rather unclear what the principles are behind the existence of adversarial perturbations, and what adversarial training does to the neural network to remove them. In this paper, we present a principle that we call Feature Purification, where we show one of the causes of the existence of adversarial examples is the accumulation of certain small dense mixtures in the hidden weights during the training process of a neural network; and more importantly, one of the goals of adversarial training is to remove such mixtures to purify hidden weights. We present both experiments on the CIFAR-10 dataset to illustrate this principle, and a theoretical result proving that for certain natural classification tasks, training a two-layer neural network with ReLU activation using randomly initialized gradient descent indeed satisfies this principle. Technically, we give, to the best of our knowledge, the first result proving that the following two can hold simultaneously for training a neural network with ReLU activation. (1) Training over the original data is indeed non-robust to small adversarial perturbations of some radius. (2) Adversarial training, even with an empirical perturbation algorithm such as FGM, can in fact be provably robust against ANY perturbations of the same radius. Finally, we also prove a complexity lower bound, showing that low complexity models such as linear classifiers, low-degree polynomials, or even the neural tangent kernel for this network, CANNOT defend against perturbations of this same radius, no matter what algorithms are used to train them.
Quantum Orbital Minimization Method for Excited States Calculation on Quantum Computer

We propose a quantum-classical hybrid variational algorithm, the quantum orbital minimization method (qOMM), for obtaining the ground state and low-lying excited states of a Hermitian operator. Given parameterized ansatz circuits representing eigenstates, qOMM implements quantum circuits to represent the objective function in the orbital minimization method and adopts classical optimizer to minimize the objective function with respect to parameters in ansatz circuits. The objective function has orthogonality implicitly embedded, which allows qOMM to apply a different ansatz circuit to each reference state. We carry out numerical simulations that seek to find excited states of the $\text{H}_{2}$, $\text{LiH}$, and a toy model consisting of 4 hydrogen atoms arranged in a square lattice in the STO-3G basis and UCCSD ansatz circuits. Comparing the numerical results with existing excited states methods, qOMM is less prone to getting stuck in local minima and can achieve convergence with more shallow ansatz circuits.
Salient Object Detection via Integrity Learning

Although current salient object detection (SOD) works have achieved significant progress, they are limited when it comes to the integrity of the predicted salient regions. We define the concept of integrity at both a micro and macro level. Specifically, at the micro level, the model should highlight all parts that belong to a certain salient object. Meanwhile, at the macro level, the model needs to discover all salient objects in a given image. To facilitate integrity learning for SOD, we design a novel Integrity Cognition Network (ICON), which explores three important components for learning strong integrity features. 1) Unlike existing models, which focus more on feature discriminability, we introduce a diverse feature aggregation (DFA) component to aggregate features with various receptive fields (i.e., kernel shape and context) and increase feature diversity. Such diversity is the foundation for mining the integral salient objects. 2) Based on the DFA features, we introduce an integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects, while suppressing the other distracting ones. 3) After extracting the enhanced features, the part-whole verification (PWV) method is employed to determine whether the part and whole object features have strong agreement. Such part-whole agreements can further improve the micro-level integrity for each salient object. To demonstrate the effectiveness of our ICON, comprehensive experiments are conducted on seven challenging benchmarks. Our ICON outperforms the baseline methods in terms of a wide range of metrics. Notably, our ICON achieves about 10% relative improvement over the previous best model in terms of average false negative ratio (FNR), on six datasets. Codes and results are available at: https://github.com/mczhuge/ICON.
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms

Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms receive increasing attention. However, OoD generalization algorithms overlook the great variance in the quality of training data, which significantly compromises the accuracy of these methods. In this paper, we theoretically reveal the relationship between training data quality and algorithm performance and analyze the optimal regularization scheme for Lipschitz regularized invariant risk minimization. A novel algorithm is proposed based on the theoretical results to alleviate the influence of low-quality data at both the sample level and the domain level. The experiments on both the regression and classification benchmarks validate the effectiveness of our method with statistical significance.
Data-Driven Invariant Learning for Probabilistic Programs

Morgan and McIver's weakest pre-expectation framework is one of the most well-established methods for deductive verification of probabilistic programs. Roughly, the idea is to generalize binary state assertions to real-valued expectations, which can measure expected values of probabilistic program quantities. While loop-free programs can be analyzed by mechanically transforming expectations, verifying loops usually requires finding an invariant expectation, a difficult task. We propose a new view of invariant expectation synthesis as a regression problem: given an input state, predict the average value of the post-expectation in the output distribution. Guided by this perspective, we develop the first data-driven invariant synthesis method for probabilistic programs. Unlike prior work on probabilistic invariant inference, our approach can learn piecewise continuous invariants without relying on template expectations, and also works with black-box access to the program. We also develop a data-driven approach to learn sub-invariants from data, which can be used to upper- or lower-bound expected values. We implement our approaches and demonstrate their effectiveness on a variety of benchmarks from the probabilistic programming literature.
Learning Imbalanced Datasets with Maximum Margin Loss

A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones. That is, underfitting for minority classes seems to be one of the challenges of generalization. For a good generalization of the minority classes, we design a new Maximum Margin (MM) loss function, motivated by minimizing a margin-based generalization bound through the shifting decision bound. The theoretically-principled label-distribution-aware margin (LDAM) loss was successfully applied with prior strategies such as re-weighting or re-sampling along with the effective training schedule. However, they did not investigate the maximum margin loss function yet. In this study, we investigate the performances of two types of hard maximum margin-based decision boundary shift with LDAM's training schedule on artificially imbalanced CIFAR-10/100 for fair comparisons and effectiveness.
The Relationship of Reading Interest with Learning Outcomes in Class IX MTS Students' Civics Subjects Single Islamic Academic Year 2017/2018

The problem of this research is whether there is a relationship between reading interest and student learning outcomes in citizenship education subjects at MTs. Islamiyah Sunggal in the 2017/2018 academic year. The purpose of this study was to determine the relationship between reading interest and student learning outcomes in citizenship education subjects at MTs. Islamiyah Sunggal in the 2017/2018 academic year. The population used in this study was class IX at MTs. Islamiyah Sunggal Medan which consisted of 4 classes with a total of 129 students. The sampling technique in this study used purposive sampling, namely the class that was the sample studied was class IX with 30 students. The instrument used is a closed questionnaire. In which this questionnaire conducted observations at MTs. Islamiyah Sunggal Medan and a questionnaire on the Relationship of Reading Interest with Student Learning Outcomes. From the results of this study, it can be seen that there is an increase in student learning outcomes with the relationship between reading interest and the research conducted, it is found that the relationship between reading interest is very influential on student learning outcomes in the subject of Citizenship Education. This is evidenced by the value of the correlation coefficient r?count = 1.094. From table r Product Moment for N = 30 obtained rtable = 0.361. So, because (1.094) &gt; (0.361) then the correlation is very strong. From the results of the calculation of the hypothesis obtained t?itung = 13.075 with a significant level = 0.05 and dk = n – 2 = 30 – 2 = 28, then get the price = 1.701. Obtained t?itung13.075&gt; 1.701. This means that Ha is accepted, meaning that there is a significant influence between the relationship between reading interest and student learning outcomes. Thus, it is concluded that there is a relationship between reading interest and student learning outcomes in civic education subjects at MTs. Islamiyah Sunggal Medan in the 2017/2018 academic year
The Use of Effective Sentences on Procedure Text for Class VII Students of Terpadu Islamic Junior High School, Batujaya

This study aims to analyze the use of effective sentences in procedural texts for seventh grade students of SMP Islam Terpadu Batujaya. This study uses a qualitative approach with a descriptive method which aims to describe objectively the use of effective sentences in students' procedural texts. The research technique used is documentation. The results showed that the students in using effective sentences were quite good. After the writer analyzed the procedural text of students in using effective sentences, the writer concluded that in the procedural text of the seventh-grade students of SMP Islam Terpadu Batujaya it was said to be quite good in the use of effective sentences. However, there are still some students who still do not understand effective sentences and there are still many students who use ambiguous sentences. The author found the use of effective sentences as many as 60 sentences. The characteristics of the effective sentence are 28 sentences (46.67%) of unity, 21 sentences (35%), 2 sentences (3.33%), 8 sentences of variation (13.33%), and parallelism. 1 sentence (1.67%).
Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning

This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users and enhance their immersive visual experiences, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited. Owing to user movement and time-varying wireless channels, the wireless VR experience enhancement problem is formulated as a sequence-dependent and mixed-integer problem with a goal of maximizing users' feeling of presence (FoP) in the virtual world, subject to power consumption constraints on access points (APs) and users' head-mounted displays (HMDs). The problem, however, is hard to be directly solved due to the lack of users' accurate tracking information and the sequence-dependent and mixed-integer characteristics. To overcome this challenge, we develop a parallel echo state network (ESN) learning method to predict users' tracking information by training fresh and historical tracking samples separately collected by APs. With the learnt results, we propose a deep reinforcement learning (DRL) based optimization algorithm to solve the formulated problem. In this algorithm, we implement deep neural networks (DNNs) as a scalable solution to produce integer decision variables and solving a continuous power control problem to criticize the integer decision variables. Finally, the performance of the proposed algorithm is compared with various benchmark algorithms, and the impact of different design parameters is also discussed. Simulation results demonstrate that the proposed algorithm is more 4.14% energy-efficient than the benchmark algorithms.