OpenRaiser/Intern-Atlas huggingface dataset:
Grammars Based on a Logic of Hypergraph Languages
Venue: arXiv cs.FL 2021
Year: 2021

The hyperedge replacement grammar (HRG) formalism is a natural and well-known generalization of context-free grammars. HRGs inherit a number of properties of context-free grammars, e.g. the pumping lemma. This lemma turns out to be a strong restriction in the hypergraph case: it implies that languages of unbounded connectivity cannot be generated by HRGs. We introduce a formalism that turns out to be more powerful than HRGs while having the same algorithmic complexity (NP-complete). Namely, we introduce hypergraph Lambek grammars; they are based on the hypergraph Lambek calculus, which may be considered as a logic of hypergraph languages. We explain the underlying principles of hypergraph Lambek grammars, establish their basic properties, and show some languages of unbounded connectivity that can be generated by them (e.g. the language of all graphs, the language of all bipartite graphs, the language of all regular graphs).
A Scalable Deep Reinforcement Learning Model for Online Scheduling Coflows of Multi-Stage Jobs for High Performance Computing
Venue: arXiv cs.DC 2021
Year: 2021

Coflow is a recently proposed networking abstraction to help improve the communication performance of data-parallel computing jobs. In multi-stage jobs, each job consists of multiple coflows and is represented by a Directed Acyclic Graph (DAG). Efficiently scheduling coflows is critical to improve the data-parallel computing performance in data centers. Compared with hand-tuned scheduling heuristics, existing work DeepWeave [1] utilizes Reinforcement Learning (RL) framework to generate highly-efficient coflow scheduling policies automatically. It employs a graph neural network (GNN) to encode the job information in a set of embedding vectors, and feeds a flat embedding vector containing the whole job information to the policy network. However, this method has poor scalability as it is unable to cope with jobs represented by DAGs of arbitrary sizes and shapes, which requires a large policy network for processing a high-dimensional embedding vector that is difficult to train. In this paper, we first utilize a directed acyclic graph neural network (DAGNN) to process the input and propose a novel Pipelined-DAGNN, which can effectively speed up the feature extraction process of the DAGNN. Next, we feed the embedding sequence composed of schedulable coflows instead of a flat embedding of all coflows to the policy network, and output a priority sequence, which makes the size of the policy network depend on only the dimension of features instead of the product of dimension and number of nodes in the job's DAG.Furthermore, to improve the accuracy of the priority scheduling policy, we incorporate the Self-Attention Mechanism into a deep RL model to capture the interaction between different parts of the embedding sequence to make the output priority scores relevant. Based on this model, we then develop a coflow scheduling algorithm for online multi-stage jobs.
An Inference Approach To Question Answering Over Knowledge Graphs
Venue: arXiv cs.LG 2021
Year: 2021

Knowledge Graphs (KG) act as a great tool for holding distilled information from large natural language text corpora. The problem of natural language querying over knowledge graphs is essential for the human consumption of this information. This problem is typically addressed by converting the natural language query to a structured query and then firing the structured query on the KG. Direct answering models over knowledge graphs in literature are very few. The query conversion models and direct models both require specific training data pertaining to the domain of the knowledge graph. In this work, we convert the problem of natural language querying over knowledge graphs to an inference problem over premise-hypothesis pairs. Using trained deep learning models for the converted proxy inferencing problem, we provide the solution for the original natural language querying problem. Our method achieves over 90% accuracy on MetaQA dataset, beating the existing state-of-the-art. We also propose a model for inferencing called Hierarchical Recurrent Path Encoder(HRPE). The inferencing models can be fine-tuned to be used across domains with less training data. Our approach does not require large domain-specific training data for querying on new knowledge graphs from different domains.
AmPyfier: Test Amplification in Python
Venue: arXiv cs.SE 2021
Year: 2021

Test Amplification is a method to extend handwritten tests into a more rigorous test suite covering corner cases in the system under test. Unfortunately, the current state-of-the-art for test amplification heavily relies on program analysis techniques which benefit a lot from explicit type declarations present in statically typed languages like Java and C++. In dynamically typed languages, such type declarations are not available and as a consequence test amplification has yet to find its way to programming languages like Python, Ruby and Javascript. In this paper, we present AmPyfier, a proof-of-concept tool, which brings test amplification to the dynamically typed, interpreted language Python. We evaluated this approach on 7 open-source projects, and found that AmPyfier could successfully strengthen 7 out of 10 test classes (70%). As such we demonstrate that test amplification is feasible for one of the most popular programming languages in use today.
A Machine-Checked Direct Proof of the Steiner-Lehmus Theorem
Venue: arXiv cs.LO 2021
Year: 2021

A direct proof of the Steiner-Lehmus theorem has eluded geometers for over 170 years. The challenge has been that a proof is only considered direct if it does not rely on reductio ad absurdum. Thus, any proof that claims to be direct must show, going back to the axioms, that all of the auxiliary theorems used are also proved directly. In this paper, we give a proof of the Steiner-Lehmus theorem that is guaranteed to be direct. The evidence for this claim is derived from our methodology: we have formalized a constructive axiom set for Euclidean plane geometry in a proof assistant that implements a constructive logic and have built the proof of the Steiner-Lehmus theorem on this constructive foundation.
Best Privacy Practice Recommendations for Global Audio Streaming Platforms
Venue: arXiv cs.CY 2021
Year: 2021

Spoon Radio is a rapidly growing global audio streaming platform which currently operates in South Korea, the United States, Japan as well as the Middle East and North Africa. The platform believes that its commitment to user privacy is an important competitive factor. As such, it aims to not just comply with existing privacy regulations in regions where it operates today but to also ensure that it anticipates likely evolution to these regulations and of user expectations. In doing so, Spoon Radio wants to ensure it is well prepared to continue its expansion into new markets. As part of an effort to inform the evolution of its data practices, Spoon Radio reached out to the Privacy Engineering Program at CMU and sponsored a capstone project in which two master's students in the Program worked with Spoon Radio personnel over the course of the 2021 Fall Semester. The present report summarizes best practice recommendations that have emerged from this collaboration. These best practices are a combination of practices that are already implemented or in the process of being implemented by Spoon Radio today as well as more aspirational recommendations, which are expected to help inform Spoon Radio's practices in the future. In this report, best practice recommendations are organized around four stages of the data life cycle: data collection, data storage, data usage, and finally data destruction. A separate section is devoted to content moderation, an area where platforms such as Spoon Radio need to reconcile considerations such as promoting freedom of expression with the need to create a safe and respectful environment that complies with applicable laws and respects relevant cultural values.
Value Activation for Bias Alleviation: Generalized-activated Deep Double Deterministic Policy Gradients
Venue: arXiv cs.LG 2021
Year: 2021

It is vital to accurately estimate the value function in Deep Reinforcement Learning (DRL) such that the agent could execute proper actions instead of suboptimal ones. However, existing actor-critic methods suffer more or less from underestimation bias or overestimation bias, which negatively affect their performance. In this paper, we reveal a simple but effective principle: proper value correction benefits bias alleviation, where we propose the generalized-activated weighting operator that uses any non-decreasing function, namely activation function, as weights for better value estimation. Particularly, we integrate the generalized-activated weighting operator into value estimation and introduce a novel algorithm, Generalized-activated Deep Double Deterministic Policy Gradients (GD3). We theoretically show that GD3 is capable of alleviating the potential estimation bias. We interestingly find that simple activation functions lead to satisfying performance with no additional tricks, and could contribute to faster convergence. Experimental results on numerous challenging continuous control tasks show that GD3 with task-specific activation outperforms the common baseline methods. We also uncover a fact that fine-tuning the polynomial activation function achieves superior results on most of the tasks.
An Autonomous Self-Incremental Learning Approach for Detection of Cyber Attacks on Unmanned Aerial Vehicles (UAVs)
Venue: arXiv cs.CR 2021
Year: 2021

As the technological advancement and capabilities of automated systems have increased drastically, the usage of unmanned aerial vehicles for performing human-dependent tasks without human indulgence has also spiked. Since unmanned aerial vehicles are heavily dependent on Information and Communication Technology, they are highly prone to cyber-attacks. With time more advanced and new attacks are being developed and employed. However, the current Intrusion detection system lacks detection and classification of new and unknown attacks. Therefore, for having an autonomous and reliable operation of unmanned aerial vehicles, more robust and automated cyber detection and protection schemes are needed. To address this, we have proposed an autonomous self-incremental learning architecture, capable of detecting known and unknown cyber-attacks on its own without any human interference. In our approach, we have combined signature-based detection along with anomaly detection in such a way that the signature-based detector autonomously updates its attack classes with the help of an anomaly detector. To achieve this, we have implemented an incremental learning approach, updating our model to incorporate new classes without forgetting the old ones. To validate the applicability and effectiveness of our proposed architecture, we have implemented it in a trial scenario and then compared it with the traditional offline learning approach. Moreover, our anomaly-based detector has achieved a 100% detection rate for attacks.
Image quality enhancement of embedded holograms in holographic information hiding using deep neural networks
Venue: arXiv cs.CV 2021
Year: 2021

Holographic information hiding is a technique for embedding holograms or images into another hologram, used for copyright protection and steganography of holograms. Using deep neural networks, we offer a way to improve the visual quality of embedded holograms. The brightness of an embedded hologram is set to a fraction of that of the host hologram, resulting in a barely damaged reconstructed image of the host hologram. However, it is difficult to perceive because the embedded hologram's reconstructed image is darker than the reconstructed host image. In this study, we use deep neural networks to restore the darkened image.
Adding semantics to measurements: Ontology-guided, systematic performance analysis
Venue: arXiv cs.PF 2021
Year: 2021

The design and operation of modern software systems exhibit a shift towards virtualization, containerization and service-based orchestration. Performance capacity engineering and resource utilization tuning become priority requirements in such environments. Measurement-based performance evaluation is the cornerstone of capacity engineering and designing for performance. Moreover, the increasing complexity of systems necessitates rigorous performance analysis approaches. However, empirical performance analysis lacks sophisticated model-based support similar to the functional design of the system. The paper proposes an ontology-based approach for facilitating and guiding the empirical evaluation throughout its various steps. Hyperledger Fabric (HLF), an open-source blockchain platform by the Linux Foundation, is modelled and evaluated as a pilot example of the approach, using the standard TPC-C performance benchmark workload.
Porting a benchmark with a classic workload to blockchain: TPC-C on Hyperledger Fabric
Venue: arXiv cs.PF 2021
Year: 2021

Many cross-organization cooperation applications of blockchain-based distributed ledger technologies (DLT) do not aim at innovation at the cooperation pattern level: essentially the same ''business'' is conducted by the parties, but this time without a central party to be trusted with bookkeeping. The migration to DLT is expected to have a negative performance impact, but some DLTs, such as Hyperledger Fabric, are accepted to be much better suited performance-wise to such use cases than others. However, with the somewhat surprising, but ongoing absence of application-level performance benchmarks for DLTs, cross-DLT comparison for "classic" workloads and the evaluation of the performance impact of "blockchainification" is still ill-supported. We present the design and Hyperledger Caliper-based open implementation of a full port of the classic TPC-C benchmark to Hyperledger Fabric, complete with a structured approach for transforming the original database schema to a smart contract data model. Initial measurements about the workload characteristics that will affect the design of large-scale performance evaluations are also included.
Longitudinal Study of the Prevalence of Malware Evasive Techniques
Venue: arXiv cs.CR 2021
Year: 2021

By their very nature, malware samples employ a variety of techniques to conceal their malicious behavior and hide it from analysis tools. To mitigate the problem, a large number of different evasion techniques have been documented over the years, and PoC implementations have been collected in public frameworks, like the popular Al-Khaser. As malware authors tend to reuse existing approaches, it is common to observe the same evasive techniques in malware samples of different families. However, no measurement study has been conducted to date to assess the adoption and prevalence of evasion techniques. In this paper, we present a large-scale study, conducted by dynamically analyzing more than 180K Windows malware samples, on the evolution of evasive techniques over the years. To perform the experiments, we developed a custom Pin-based Evasive Program Profiler (Pepper), a tool capable of both detecting and circumventing 53 anti-dynamic-analysis techniques of different categories, ranging from anti-debug to virtual machine detection. To observe the phenomenon of evasion from different points of view, we employed four different datasets, including benign files, advanced persistent threat (APTs), malware samples collected over a period of five years, and a recent collection of different families submitted to VirusTotal over a one-month period.
Voice Quality and Pitch Features in Transformer-Based Speech Recognition
Venue: arXiv cs.CL 2021
Year: 2021

Jitter and shimmer measurements have shown to be carriers of voice quality and prosodic information which enhance the performance of tasks like speaker recognition, diarization or automatic speech recognition (ASR). However, such features have been seldom used in the context of neural-based ASR, where spectral features often prevail. In this work, we study the effects of incorporating voice quality and pitch features altogether and separately to a Transformer-based ASR model, with the intuition that the attention mechanisms might exploit latent prosodic traits. For doing so, we propose separated convolutional front-ends for prosodic and spectral features, showing that this architectural choice yields better results than simple concatenation of such pitch and voice quality features to mel-spectrogram filterbanks. Furthermore, we find mean Word Error Rate relative reductions of up to 5.6% with the LibriSpeech benchmark. Such findings motivate further research on the application of prosody knowledge for increasing the robustness of Transformer-based ASR.
LSH methods for data deduplication in a Wikipedia artificial dataset
Venue: arXiv cs.CL 2021
Year: 2021

This paper illustrates locality sensitive hasing (LSH) models for the identification and removal of nearly redundant data in a text dataset. To evaluate the different models, we create an artificial dataset for data deduplication using English Wikipedia articles. Area-Under-Curve (AUC) over 0.9 were observed for most models, with the best model reaching 0.96. Deduplication enables more effective model training by preventing the model from learning a distribution that differs from the real one as a result of the repeated data.
CryptoMiniSat Switches-Optimization for Solving Cryptographic Instances
Venue: arXiv cs.CR 2021
Year: 2021

Performing hundreds of test runs and a source-code analysis, we empirically identified improved parameter configurations for the CryptoMiniSat (CMS) 5 for solving cryptographic CNF instances originating from algebraic known-plaintext attacks on 3 rounds encryption of the Small AES-64 model cipher SR$(3, 4, 4, 4)$. We finally became able to reconstruct 64-bit long keys in under an hour real time which, to our knowledge, has never been achieved so far. Especially, not without any assumptions or previous knowledge of key-bits (for instance in the form of side-channels, as in \cite{Mohamed2012algebraicSCA}). A statistical analysis of the non-deterministic solver runtimes was carried out and command line parameter combinations were defined to yield best runtimes which ranged from under an hour to a few hours in median at the beginning. We proceeded using an Automatic Algorithm Configuration (AAC) tool to systematically extend the search for even better solver configurations with success to deliver even shorter solving times. In this work we elaborate on the systematics we followed to reach our results in a traceable and reproducible way. The ultimate focus of our investigations is to find out if CMS, when appropriately tuned, is indeed capable to attack even bigger and harder problems than the here solved ones. For the domain of cryptographic research, the duration of the solving time plays an inferior role as compared to the practical feasibility of finding a solution to the problem. The perspective scalability of the here presented results is the object of further investigations.
Adversarial Neural Networks for Error Correcting Codes
Venue: arXiv cs.LG 2021
Year: 2021

Error correcting codes are a fundamental component in modern day communication systems, demanding extremely high throughput, ultra-reliability and low latency. Recent approaches using machine learning (ML) models as the decoders offer both improved performance and great adaptability to unknown environments, where traditional decoders struggle. We introduce a general framework to further boost the performance and applicability of ML models. We propose to combine ML decoders with a competing discriminator network that tries to distinguish between codewords and noisy words, and, hence, guides the decoding models to recover transmitted codewords. Our framework is game-theoretic, motivated by generative adversarial networks (GANs), with the decoder and discriminator competing in a zero-sum game. The decoder learns to simultaneously decode and generate codewords while the discriminator learns to tell the differences between decoded outputs and codewords. Thus, the decoder is able to decode noisy received signals into codewords, increasing the probability of successful decoding. We show a strong connection of our framework with the optimal maximum likelihood decoder by proving that this decoder defines a Nash equilibrium point of our game. Hence, training to equilibrium has a good possibility of achieving the optimal maximum likelihood performance. Moreover, our framework does not require training labels, which are typically unavailable during communications, and, thus, seemingly can be trained online and adapt to channel dynamics. To demonstrate the performance of our framework, we combine it with the very recent neural decoders and show improved performance compared to the original models and traditional decoding algorithms on various codes.
Mixed Precision of Quantization of Transformer Language Models for Speech Recognition
Venue: arXiv cs.CL 2021
Year: 2021

State-of-the-art neural language models represented by Transformers are becoming increasingly complex and expensive for practical applications. Low-bit deep neural network quantization techniques provides a powerful solution to dramatically reduce their model size. Current low-bit quantization methods are based on uniform precision and fail to account for the varying performance sensitivity at different parts of the system to quantization errors. To this end, novel mixed precision DNN quantization methods are proposed in this paper. The optimal local precision settings are automatically learned using two techniques. The first is based on a quantization sensitivity metric in the form of Hessian trace weighted quantization perturbation. The second is based on mixed precision Transformer architecture search. Alternating direction methods of multipliers (ADMM) are used to efficiently train mixed precision quantized DNN systems. Experiments conducted on Penn Treebank (PTB) and a Switchboard corpus trained LF-MMI TDNN system suggest the proposed mixed precision Transformer quantization techniques achieved model size compression ratios of up to 16 times over the full precision baseline with no recognition performance degradation. When being used to compress a larger full precision Transformer LM with more layers, overall word error rate (WER) reductions up to 1.7% absolute (18% relative) were obtained.
Real-time Street Human Motion Capture
Venue: arXiv cs.CV 2021
Year: 2021

In recent years, motion capture technology using computers has developed rapidly. Because of its high efficiency and excellent performance, it replaces many traditional methods and is being widely used in many fields. Our project is about street scene video human motion capturing and analysis. The primary goal of the project is to capture the human motion in a video and use the motion information for 3D animation (human) in real-time. We applied a neural network for motion capture and implement it in the unity under a street view scene. By analyzing the motion data, we will have a better estimation of the street condition, which is useful for other high-tech applications such as self-driving cars.