Reviewerly/exHarmony huggingface dataset:
Compression of YOLOv3 via Block-Wise and Channel-Wise Pruning for Real-Time and Complicated Autonomous Driving Environment Sensing Applications

Nowadays, in the area of autonomous driving, the computational power of the object detectors is limited by the embedded devices and the public datasets for autonomous driving are over-idealistic. In this paper, we propose a pipeline combining both block-wise pruning and channel-wise pruning to compress the object detection model iteratively. We enforce the introduced factor of the residual blocks and the scale parameters in Batch Normalization (BN) layers to sparsity to select the less important residual blocks and channels. Moreover, a modified loss function has been proposed to remedy the class-imbalance problem. After removing the unimportant structures iteratively, we get the pruned YOLOv3 trained on our datasets which have more abundant and elaborate classes. Evaluated by our validation sets on the server, the pruned YOLOv3 saves 79.7% floating point operations (FLOPs), 93.8% parameter size, 93.8% model volume and 45.4% inference times with only 4.16% mean of average precision (mAP) loss. Evaluated on the embedded device, the pruned model operates about 13 frames per second with 4.53% mAP loss. These results show that the real-time property and accuracy of the pruned YOLOv3 can meet the needs of the embedded devices in complicated autonomous driving environments.
Selecting Correct Methods to Extract Fuzzy Rules from Artificial Neural Network

Artificial neural network (ANN) inherently cannot explain in a comprehensible form how a given decision or output is generated, which limits its extensive use. Fuzzy rules are an intuitive and reasonable representation to be used for explanation, model checking, and system integration. However, different methods may extract different rules from the same ANN. Which one can deliver good quality such that the ANN can be accurately described by the extracted fuzzy rules? In this paper, we perform an empirical study on three different rule extraction methods. The first method extracts fuzzy rules from a fuzzy neural network, while the second and third ones are originally designed to extract crisp rules, which can be transformed into fuzzy rules directly, from a well-trained ANN. In detail, in the second method, the behavior of a neuron is approximated by (continuous) Boolean functions with respect to its direct input neurons, whereas in the third method, the relationship between a neuron and its direct input neurons is described by a decision tree. We evaluate the three methods on discrete, continuous, and hybrid data sets by comparing the rules generated from sample data directly. The results show that the first method cannot generate proper fuzzy rules on the three kinds of data sets, the second one can generate accurate rules on discrete data, while the third one can generate fuzzy rules for all data sets but cannot always guarantee the accuracy, especially for data sets with poor separability. Hence, our work illustrates that, given an ANN, one should carefully select a method, sometimes even needs to design new methods for explanations.
A new exact algorithm for the shortest path problem: An optimized shortest distance matrix

The growing amount of data generated from the increasingly sophisticated network connections requires greater accuracy and higher efficiency in pinpointing the shortest paths concerned. Therefore, the earlier classical exact algorithms are no longer 100 percent suitable for large-scale data processing, for their known great time complexity during calculation. In this paper, We present an updated shortest distance matrix (SDM) algorithm. Evidence to the operation's properties is provided and the properties are used in subsequent optimizations. Compared with Dijkstra's algorithm and Floyd's algorithm, the optimized SDM algorithm with parallel mode makes a great improvement in shortening the running time. The data test shows that the new algorithm improves the efficiency in processing a large amount of data.
Deep Reinforcement Learning for Autonomous Driving by Transferring Visual Features

Deep reinforcement learning (DRL) has achieved great success in processing vision-based driving tasks. However, the end-to-end training manner makes DRL agents suffer from overfitting training scenes. The agents easily fail to generalize to unseen environments. In this paper, we propose a deep reinforcement learning for autonomous driving by transferring visual features. We formulate the DRL training as a perception and control module and introduce adversarial training mechanism for autonomous driving. The perception module is able to extract invariant features between different domains through adversarial training. While the DRL agent can then be trained on the basis of low dimensional states. In this manner, the proposed approach enables trained agents to adapt to unseen environments by learning robust features invariant across various scenes. We evaluate the proposed approach by transferring visual features between different simulators. The experimental results demonstrate the driving policy trained in the source domain can be directly applied in the target domain, and achieve great efficient and effective performance for autonomous driving.
APIScanner - Towards Automated Detection of Deprecated APIs in Python Libraries

Python libraries are widely used for machine learning and scientific computing tasks today. APIs in Python libraries are deprecated due to feature enhancements and bug fixes in the same way as in other languages. These deprecated APIs are discouraged from being used in further software development. Manually detecting and replacing deprecated APIs is a tedious and time-consuming task due to the large number of API calls used in the projects. Moreover, the lack of proper documentation for these deprecated APIs makes the task challenging. To address this challenge, we propose an algorithm and a tool APIScanner that automatically detects deprecated APIs in Python libraries. This algorithm parses the source code of the libraries using abstract syntax tree (ASTs) and identifies the deprecated APIs via decorator, hard-coded warning or comments. APIScanner is a Visual Studio Code Extension that highlights and warns the developer on the use of deprecated API elements while writing the source code. The tool can help developers to avoid using deprecated API elements without the execution of code. We tested our algorithm and tool on six popular Python libraries, which detected 838 of 871 deprecated API elements. Demo of APIScanner: https://youtu.be/1hy_ugf-iek. Documentation, tool, and source code can be found here: https://rishitha957.github.io/APIScanner.
Using Barcode to Track Student Attendance and Assets in Higher Education Institutions

Most instructors and staff in a university environment use manual methods of tracking student attendance and college assets. However, these methods have several disadvantages; the major drawbacks of taking attendance manually are errors in data collection and the loss of part of the lecture time. As for asset tracking, the common issues are human errors in data collection, wrong inventory estimation and time wastage during the process. Automated identification and data capturing technologies can help address these issues. This paper investigates the implementation of one such system, the barcode technology, to read and process student attendance data in classroom settings and to track periodic inventory of assets within the college facilities such as devices, equipment, office furniture, classrooms, and laboratories. The paper measures faculty members and staff response on usability, comfort level, and efficiency for the proposed barcode system. This work shows that barcode technology eliminates data entry errors and improves efficiency for entering data with a minimum cost of creating barcode tags and hardware.
Continuous Face Aging Generative Adversarial Networks

Face aging is the task aiming to translate the faces in input images to designated ages. To simplify the problem, previous methods have limited themselves only able to produce discrete age groups, each of which consists of ten years. Consequently, the exact ages of the translated results are unknown and it is unable to obtain the faces of different ages within groups. To this end, we propose the continuous face aging generative adversarial networks (CFA-GAN). Specifically, to make the continuous aging feasible, we propose to decompose image features into two orthogonal features: the identity and the age basis features. Moreover, we introduce the novel loss function for identity preservation which maximizes the cosine similarity between the original and the generated identity basis features. With the qualitative and quantitative evaluations on MORPH, we demonstrate the realistic and continuous aging ability of our model, validating its superiority against existing models. To the best of our knowledge, this work is the first attempt to handle continuous target ages.
Multi-Rate Attention Architecture for Fast Streamable Text-to-Speech Spectrum Modeling

Typical high quality text-to-speech (TTS) systems today use a two-stage architecture, with a spectrum model stage that generates spectral frames and a vocoder stage that generates the actual audio. High-quality spectrum models usually incorporate the encoder-decoder architecture with self-attention or bi-directional long short-term (BLSTM) units. While these models can produce high quality speech, they often incur O(L) increase in both latency and real-time factor (RTF) with respect to input length L. In other words, longer inputs leads to longer delay and slower synthesis speed, limiting its use in real-time applications. In this paper, we propose a multi-rate attention architecture that breaks the latency and RTF bottlenecks by computing a compact representation during encoding and recurrently generating the attention vector in a streaming manner during decoding. The proposed architecture achieves high audio quality (MOS of 4.31 compared to groundtruth 4.48), low latency, and low RTF at the same time. Meanwhile, both latency and RTF of the proposed system stay constant regardless of input lengths, making it ideal for real-time applications.
Causality‐based online streaming feature selection

Abstract Online streaming feature selection, as a well‐known and effective preprocessing approach in machine learning, is an eternal topic. Amount of online streaming feature selection algorithms have achieved a great deal of success in classification and prediction tasks. However, most of these existing algorithms only concentrate on the relevance between features and labels, and neglect the causal relationships between them. Discovering the potential causal relationships between features and labels, that is, the Markov blanket (MB) of class label, which can build a more interpretable and robust classification model. In this paper, we put forward a causality‐based online streaming feature selection algorithm with neighborhood conditional mutual information. First, we apply neighborhood symmetrical uncertainty to discover a candidate Markov blanket (CMB) with causal information. Then, neighborhood conditional mutual information instead of conditional independence test is used to delete the false positives in CMB, which can significantly alleviate the computational cost. Moreover, we utilize the updated CMB to choose the true spouses, which may be mistakenly deleted during the process of removing false positives, and then acquire an optimal MB as the online selected feature subset. Finally, causality‐based online streaming feature selection with neighborhood conditional mutual information is compared with four well‐established online streaming feature selection methods on 13 real‐world datasets. Experiment results show that the proposed algorithm outperforms these online streaming feature selection algorithms.
On Syntactic Forgetting Under Uniform Equivalence

Forgetting in Answer Set Programming (ASP) aims at reducing the language of a logic program without affecting the consequences over the remaining language. It has recently gained interest in the context of modular ASP where it allows simplifying a program of a module, making it more declarative, by omitting auxiliary atoms or hiding certain atoms/parts of the program not to be disclosed. Unlike for arbitrary programs, it has been shown that forgetting for modular ASP can always be applied, for input, output and hidden atoms, and preserve all dependencies over the remaining language (in line with uniform equivalence). However, the definition of the result is based solely on a semantic characterization in terms of HT-models. Thus, computing an actual result is a complicated process and the result commonly bears no resemblance to the original program, i.e., we are lacking a corresponding syntactic operator. In this paper, we show that there is no forgetting operator that preserves uniform equivalence (modulo the forgotten atoms) between the given program and its forgetting result by only manipulating the rules of the original program that contain the atoms to be forgotten. We then present a forgetting operator that preserves uniform equivalence and is syntactic whenever this is suitable. We also introduce a special class of programs, where syntactic forgetting is always possible, and as a complementary result, establish it as the largest known class where forgetting while preserving all dependencies is always possible.
Exploring the RISC-V Vector Extension for the Classic McEliece Post-Quantum Cryptosystem

The dawn of quantum computers threatens the security guarantees of classical public-key cryptography. This gave rise to a new class of so-called quantum-resistant cryptography algorithms and a need to efficiently implement them on embedded hardware platforms. This paper investigates how we can exploit the most recent RISC-V Vector Extension Version 0.9 (RVV0.9) to accelerate the quantum-resistant code-based Classic McEliece cryptosystem. We focused on the Gaussian Elimination Algorithm (GEA) that is essential for the key generation of the McEliece scheme. The GEA offers high potential for acceleration by vector instructions of the RVV extension. In order to evaluate the possible gains, we adopted a rapid prototyping approach based on an instruction set simulator (ISS). We extended the simulator ETISS with a SoftVector library, which allows to quickly model the instructions of RVV. Using the rapid prototyping environment, the GEA was re-implemented and verified for RVV0.9.The final performance gain heavily depends on the memory interface of the vector unit. For different configurations of the memory system, we could profile performance gains of 6 up to 18 for the GEA. This clearly shows the benefit of RVV for implementing quantum-resistant cryptosystems.
Priorities, Technology, &amp; Power: Co-Designing an Inclusive Transit Agenda in Kampala, Uganda

There is considerable effort within the HCI community to explore, document, and advocate for the lived experiences of persons with disabilities (PWDs). However, PWDs from the Global South, particularly Africa, are underrepresented in this scholarship. We contribute to closing this gap by investigating the unmet transit needs and characterization of technology within the disability community in Kampala, Uganda. We investigated transportation due to the increase in ride-share solutions created by widespread mobile computing and the resulting disruption of transportation worldwide. We hosted co-design sessions with disability advocates and adapted the stakeholder tokens method from the value-sensitive design framework to map the stakeholder ecosystem. Our key insight is the identification of a new group of non-traditional core stakeholders who highlight the values of inclusion, mobility, and safety within the ecosystem. Finally, we discuss how our findings engage with concepts of disability justice and perceptions of power.
Lanterns

Previous HCI research has highlighted opportunities for digital technologies to support outdoor play amongst children. However, the tendency has been to focus on older children and forms of play that are structured and rule-based. We report on a Research-through-Design (RtD) inquiry, grounded in an Embodied Interactional approach, that investigated configurations of off-the-shelf Internet of Things (IoT) tool-kits to inspire new forms of free play outdoors for preschool children. We designed the Lanterns, a tangible interactive resource that is made using household materials and guided by a template, and which explores new possibilities to inspire social play and embodied interaction outdoors. Based on observations of the Lanterns being used by preschool children and Early Years Practitioners outdoors, we identify qualities of free play promoted by the Lanterns outdoors, such as enchantment, improvisation, anticipation and choice. We discuss our findings by defining three sensitising concepts to support future design research in this space: Choosing the Way; Improvising through Movement; Anticipating a Response.
Long short-term memory self-adapting online random forests for evolving data stream regression

Evolving data stream, especially with concept drift is generally accepted as a challenging data type for regression task, because it usually makes machine learning models trained on old data not adapting to new data, and leads to dramatic performance degradation as a result. Moreover, the behavior of a data stream may change in different modes and therefore introduces various concept drifts, e.g., abrupt, incremental, gradual, recurring, even more, complex concept drifts. Although there are some algorithms that can adapt to stationary data streams or a specific type of concept drift in non-stationary data streams, a wide range of practical applications call for machine learning regression models to handle multi-type of data streams. In this work, we propose an online learning strategy called adaptive long and short-term memories online Random Forests regression(ALSM-RFR), where an adaptive memory activation mechanism is designed to make the model switch adaptively between long-term and hybrid (long-term plus short-term) memory modes in the face of stationary data streams or non-stationary data streams with different types of concept drift. In particular, leaf and tree weights in random forests are used to learn information at different timescales, namely, long-term and short-term memories. Moreover, we devise an adaptive memory activation mechanism to formulate the switch decision of memory modes as a classification problem. Numerical experiments show remarkable improvements of the proposed method in the adaptability of stream types and predictive accuracy in data streams across several real datasets and synthetic datasets, compared to the state-of-the-art online approaches. Besides, the convergence and the influence of the parameters involved in our method are evaluated.
Resource Management and Pricing for Cloud Computing Based Mobile Blockchain With Pooling

In a public blockchain system applying Proof of Work (PoW), the participants need to compete with their computing resources for reward, which is challenging for resource-limited devices. Mobile blockchain is proposed to facilitate the application of blockchain for mobile service, in which the lightweight devices can participate mining by renting resources from the Cloud Computing Service Provider (CCSP), but CCSP usually does not have the information about the demand preference of users. In this article, a contract model is adopted to address the cloud computing resource allocation and pricing problem in the mobile blockchain. In particular, an adverse selection contract solution is proposed to overcome the information asymmetry problem, and resource pooling is introduced to improve the stability of users’ rewards. Simulation results show that the information asymmetry problem is well overcome by adverse selection contract so that CCSP can obtain more utility than linear pricing contracts. Furthermore, the resource pooling could effectively improve the users’ and CCSP's utilities. When the size of the mining pool is large enough, it can achieve an improvement effect of more than 10 times. The effect of pool size and user type distribution on CCSP's utility is also studied.
Finding Metamorphic Relations for Scientific Software

Metamorphic testing uncovers defects by checking whether a relation holds among multiple software executions. These relations are known as metamorphic relations (MRs). For scientific software operating in a large multi-parameter input space, identifying MRs that determine the simultaneous changes among multiple variables is challenging. In this poster, we propose a fully automatic approach to classifying input and output variables from scientific software's user manual, mining these variables' associations from the user forum to generate MRs, and validating the MRs with existing regression tests. Preliminary results of our end-to-end MR support for the Storm Water Management Model (SWMM) are reported.
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study

Handling digital images is almost always accompanied by a lossy compression in order to facilitate efficient transmission and storage. This introduces an unavoidable tension between the allocated bit-budget (rate) and the faithfulness of the resulting image to the original one (distortion). An additional complicating consideration is the effect of the compression on recognition performance by given classifiers (accuracy). This work aims to explore this rate-distortion-accuracy tradeoff. As a case study, we focus on the design of the quantization tables in the JPEG compression standard, offering a novel optimal tuning of these tables, leveraging a differential implementation of both the JPEG encoder-decoder and an entropy estimator. This enables us to offer a unified framework that considers the interplay between rate, distortion and classification accuracy. In all these fronts, we report a substantial boost in performance by a simple and easily implemented modification of these tables.
Patnet : A Phoneme-Level Autoregressive Transformer Network for Speech Synthesis

Aiming at efficiently predicting acoustic features with high naturalness and robustness, this paper proposes PATNet, a neural acoustic model for speech synthesis using phoneme-level autoregression. PATNet accepts phoneme sequences as input and is built based on Transformer structure. PATNet adopts a duration model instead of attention mechanism for sequence alignment. The decoder of PATNet predicts multi-frame spectra within one phoneme in parallel given the predicted spectra of previous phonemes. Such phoneme-level autoregression enables PATNet to achieve higher inference efficiency than the models with frame-level autoregression, such as Transformer-TTS, and improves the robustness of acoustic feature prediction by utilizing phoneme boundaries explicitly. Experimental results show that the speech synthesized by PATNet obtained lower character error rate (CER) than Tacotron, Transfomer-TTS and FastSpeech when evaluated by a speech recognition engine. Besides, PATNet achieved 10 times faster inference speed than Transformer-TTS and significantly better naturalness than FastSpeech.