OpenRaiser/Intern-Atlas huggingface dataset:
Feature-based Representation for Violin Bridge Admittances
Venue: arXiv cs.SD 2021
Year: 2021

Frequency Response Functions (FRFs) are one of the cornerstones of musical acoustic experimental research. They describe the way in which musical instruments vibrate in a wide range of frequencies and are used to predict and understand the acoustic differences between them. In the specific case of stringed musical instruments such as violins, FRFs evaluated at the bridge are known to capture the overall body vibration. These indicators, also called bridge admittances, are widely used in the literature for comparative analyses. However, due to their complex structure they are rather difficult to quantitatively compare and study. In this manuscript we present a way to quantify differences between FRFs, in particular violin bridge admittances, that separates the effects in frequency, amplitude and quality factor of the first resonance peaks characterizing the responses. This approach allows us to define a distance between FRFs and clusterise measurements according to this distance. We use two case studies, one based on Finite Element Analysis and another exploiting measurements on real violins, to prove the effectiveness of such representation. In particular, for simulated bridge admittances the proposed distance is able to highlight the different impact of consecutive simulation `steps' on specific vibrational properties and, for real violins, gives a first insight on similar styles of making, as well as opposite ones.
Representation, Analysis of Bayesian Refinement Approximation Network: A Survey
Venue: arXiv cs.CV 2021
Year: 2021

After an artificial model background subtraction, the pixels have been labelled as foreground and background. Previous approaches to secondary processing the output for denoising usually use traditional methods such as the Bayesian refinement method. In this paper, we focus on using a modified U-Net model to approximate the result of the Bayesian refinement method and improve the result. In our modified U-Net model, the result of background subtraction from other models will be combined with the source image as input for learning the statistical distribution. Thus, the losing information caused by the background subtraction model can be restored from the source image. Moreover, since the part of the input image is already the output of the other background subtraction model, the feature extraction should be convenient, it only needs to change the labels of the noise pixels. Compare with traditional methods, using deep learning methods superiority in keeping details.
Cache-Efficient Fork-Processing Patterns on Large Graphs
Venue: arXiv cs.DB 2021
Year: 2021

As large graph processing emerges, we observe a costly fork-processing pattern (FPP) that is common in many graph algorithms. The unique feature of the FPP is that it launches many independent queries from different source vertices on the same graph. For example, an algorithm in analyzing the network community profile can execute Personalized PageRanks that start from tens of thousands of source vertices at the same time. We study the efficiency of handling FPPs in state-of-the-art graph processing systems on multi-core architectures. We find that those systems suffer from severe cache miss penalty because of the irregular and uncoordinated memory accesses in processing FPPs. In this paper, we propose ForkGraph, a cache-efficient FPP processing system on multi-core architectures. To improve the cache reuse, we divide the graph into partitions each sized of LLC capacity, and the queries in an FPP are buffered and executed on the partition basis. We further develop efficient intra- and inter-partition execution strategies for efficiency. For intra-partition processing, since the graph partition fits into LLC, we propose to execute each graph query with efficient sequential algorithms (in contrast with parallel algorithms in existing parallel graph processing systems) and present an atomic-free query processing by consolidating contending operations to cache-resident graph partition. For inter-partition processing, we propose yielding and priority-based scheduling, to reduce redundant work in processing. Besides, we theoretically prove that ForkGraph performs the same amount of work, to within a constant factor, as the fastest known sequential algorithms in FPP queries processing, which is work efficient. Our evaluations on real-world graphs show that ForkGraph significantly outperforms state-of-the-art graph processing systems with two orders of magnitude speedups.
Self-supervised Graph Neural Networks without explicit negative sampling
Venue: arXiv cs.LG 2021
Year: 2021

Real world data is mostly unlabeled or only few instances are labeled. Manually labeling data is a very expensive and daunting task. This calls for unsupervised learning techniques that are powerful enough to achieve comparable results as semi-supervised/supervised techniques. Contrastive self-supervised learning has emerged as a powerful direction, in some cases outperforming supervised techniques. In this study, we propose, SelfGNN, a novel contrastive self-supervised graph neural network (GNN) without relying on explicit contrastive terms. We leverage Batch Normalization, which introduces implicit contrastive terms, without sacrificing performance. Furthermore, as data augmentation is key in contrastive learning, we introduce four feature augmentation (FA) techniques for graphs. Though graph topological augmentation (TA) is commonly used, our empirical findings show that FA perform as good as TA. Moreover, FA incurs no computational overhead, unlike TA, which often has O(N^3) time complexity, N-number of nodes. Our empirical evaluation on seven publicly available real-world data shows that, SelfGNN is powerful and leads to a performance comparable with SOTA supervised GNNs and always better than SOTA semi-supervised and unsupervised GNNs. The source code is available at https://github.com/zekarias-tilahun/SelfGNN.
Explaining the Road Not Taken
Venue: arXiv cs.CL 2021
Year: 2021

It is unclear if existing interpretations of deep neural network models respond effectively to the needs of users. This paper summarizes the common forms of explanations (such as feature attribution, decision rules, or probes) used in over 200 recent papers about natural language processing (NLP), and compares them against user questions collected in the XAI Question Bank. We found that although users are interested in explanations for the road not taken -- namely, why the model chose one result and not a well-defined, seemly similar legitimate counterpart -- most model interpretations cannot answer these questions.
Dynamic Information Sharing and Punishment Strategies
Venue: arXiv cs.GT 2021
Year: 2021

In this paper we study the problem of information sharing among rational self-interested agents as a dynamic game of asymmetric information. We assume that the agents imperfectly observe a Markov chain and they are called to decide whether they will share their noisy observations or not at each time instant. We utilize the notion of conditional mutual information to evaluate the information being shared among the agents. The challenges that arise due to the inter-dependence of agents' information structure and decision-making are exhibited. For the finite horizon game we prove that agents do not have incentive to share information. In contrast, we show that cooperation can be sustained in the infinite horizon case by devising appropriate punishment strategies which are defined over the agents' beliefs on the system state. We show that these strategies are closed under the best-response mapping and that cooperation can be the optimal choice in some subsets of the state belief simplex. We characterize these equilibrium regions, prove uniqueness of a maximal equilibrium region and devise an algorithm for its approximate computation.
A Survey on Ethical Hacking: Issues and Challenges
Venue: arXiv cs.CR 2021
Year: 2021

Security attacks are growing in an exponential manner and their impact on existing systems is seriously high and can lead to dangerous consequences. However, in order to reduce the effect of these attacks, penetration tests are highly required, and can be considered as a suitable solution for this task. Therefore, the main focus of this paper is to explain the technical and non-technical steps of penetration tests. The objective of penetration tests is to make existing systems and their corresponding data more secure, efficient and resilient. In other terms, pen testing is a simulated attack with the goal of identifying any exploitable vulnerability or/and a security gap. In fact, any identified exploitable vulnerability will be used to conduct attacks on systems, devices, or personnel. This growing problem should be solved and mitigated to reach better resistance against these attacks. Moreover, the advantages and limitations of penetration tests are also listed. The main issue of penetration tests that it is efficient to detect known vulnerabilities. Therefore, in order to resist unknown vulnerabilities, a new kind of modern penetration tests is required, in addition to reinforcing the use of shadows honeypots. This can also be done by reinforcing the anomaly detection of intrusion detection/prevention system. In fact, security is increased by designing an efficient cooperation between the different security elements and penetration tests.
Playing Against the Board: Rolling Horizon Evolutionary Algorithms Against Pandemic
Venue: arXiv cs.AI 2021
Year: 2021

Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling horizon evolutionary algorithm designed specifically for this game. The complex way in which the Pandemic game state changes in a stochastic but predictable way required a number of specially designed forward models, macro-action representations for decision-making, and repair functions for the genetic operations of the evolutionary algorithm. Variants of the algorithm which explore optimistic versus pessimistic game state evaluations, different mutation rates and event horizons are compared against a baseline hierarchical policy agent. Results show that an evolutionary approach via short-horizon rollouts can better account for the future dangers that the board may introduce, and guard against them. Results highlight the types of challenges that collaborative board games pose to artificial intelligence, especially for handling multi-player collaboration interactions.
The General Theory of General Intelligence: A Pragmatic Patternist Perspective
Venue: arXiv cs.AI 2021
Year: 2021

A multi-decade exploration into the theoretical foundations of artificial and natural general intelligence, which has been expressed in a series of books and papers and used to guide a series of practical and research-prototype software systems, is reviewed at a moderate level of detail. The review covers underlying philosophies (patternist philosophy of mind, foundational phenomenological and logical ontology), formalizations of the concept of intelligence, and a proposed high level architecture for AGI systems partly driven by these formalizations and philosophies. The implementation of specific cognitive processes such as logical reasoning, program learning, clustering and attention allocation in the context and language of this high level architecture is considered, as is the importance of a common (e.g. typed metagraph based) knowledge representation for enabling "cognitive synergy" between the various processes. The specifics of human-like cognitive architecture are presented as manifestations of these general principles, and key aspects of machine consciousness and machine ethics are also treated in this context. Lessons for practical implementation of advanced AGI in frameworks such as OpenCog Hyperon are briefly considered.
Phism: Polyhedral High-Level Synthesis in MLIR
Venue: arXiv cs.PL 2021
Year: 2021

Polyhedral optimisation, a methodology that views nested loops as polyhedra and searches for their optimal transformation regarding specific objectives (parallelism, locality, etc.), sounds promising for mitigating difficulties in automatically optimising hardware designs described by high-level synthesis (HLS), which are typically software programs with nested loops. Nevertheless, existing polyhedral tools cannot meet the requirements from HLS developers for platform-specific customisation and software/hardware co-optimisation. This paper proposes $ϕ_{sm}$ (phism), a polyhedral HLS framework built on MLIR, to address these challenges through progressive lowering multi-level intermediate representations (IRs) from polyhedra to HLS designs.
Face Recognition as a Method of Authentication in a Web-Based System
Venue: arXiv cs.CV 2021
Year: 2021

Online information systems currently heavily rely on the username and password traditional method for protecting information and controlling access. With the advancement in biometric technology and popularity of fields like AI and Machine Learning, biometric security is becoming increasingly popular because of the usability advantage. This paper reports how machine learning based face recognition can be integrated into a web-based system as a method of authentication to reap the benefits of improved usability. This paper includes a comparison of combinations of detection and classification algorithms with FaceNet for face recognition. The results show that a combination of MTCNN for detection, Facenet for generating embeddings, and LinearSVC for classification outperforms other combinations with a 95% accuracy. The resulting classifier is integrated into the web-based system and used for authenticating users.
Image Processing Techniques for identifying tumors in an MRI image
Venue: arXiv cs.CV 2021
Year: 2021

Medical Resonance Imaging or MRI is a medical image processing technique that used radio waves to scan the body. It is a tomographic imaging technique, principally used in the field of radiology. With the advantage of being a painless diagnostic procedure, MRI allows medical personnel to illustrate clear pictures of the anatomy and the physiological processes occurring in the body, thus allowing early detection and treatment of diseases. These images, combined with image processing techniques may be used in the detection of tumors, difficult to identify with the naked eye. This digital assignment surveys the different image processing techniques used in Automated Tumor Detection (ATD). This assignment initiates the discussion with a comparison of traditional techniques such as Morphological Tools (MT) and Region Growing Technique (RGT).
Family Column Generation: A Principled Stabilized Column Generation Approach
Venue: arXiv cs.DS 2021
Year: 2021

We tackle the problem of accelerating column generation (CG) approaches to set cover formulations in operations research. At each iteration of CG we generate a dual solution that approximately solves the LP over all columns consisting of a subset of columns in the nascent set. We refer to this linear program (LP) as the Family Restricted Master Problem (FRMP), which provides a tighter bound on the master problem at each iteration of CG, while preserving efficient inference. For example, in the single source capacitated facility location problem (SSCFLP) the family of a column $l$ associated with facility $f$ and customer set $N_l$ contains the set of columns associated with $f$ and the customer set that lies in the power set of $N_l$. The solution to FRMP optimization is attacked with a coordinate ascent method in the dual. The generation of direction of travel corresponds to solving the restricted master problem over columns corresponding to the reduced lowest cost column in each family given specific dual variables based on the incumbent dual, and is easily generated without resolving complex pricing problems. We apply our algorithm to the SSCFLP and demonstrate improved performance over two relevant baselines.
Real World Scrum A Grounded Theory of Variations in Practice
Venue: arXiv cs.SE 2021
Year: 2021

Scrum, the most popular agile method and project management framework, is widely reported to be used, adapted, misused, and abused in practice. However, not much is known about how Scrum actually works in practice, and critically, where, when, how and why it diverges from Scrum by the book. Through a Grounded Theory study involving semi-structured interviews of 45 participants from 30 companies and observations of five teams, we present our findings on how Scrum works in practice as compared to how it is presented in its formative books. We identify significant variations in these practices such as work breakdown, estimation, prioritization, assignment, the associated roles and artefacts, and discuss the underlying rationales driving the variations. Critically, we claim that not all variations are process misuse/abuse and propose a nuanced classification approach to understanding variations as standard, necessary, contextual, and clear deviations for successful Scrum use and adaptation
Dynamic Binary Translation for SGX Enclaves
Venue: arXiv cs.CR 2021
Year: 2021

Enclaves, such as those enabled by Intel SGX, offer a hardware primitive for shielding user-level applications from the OS. While enclaves are a useful starting point, code running in the enclave requires additional checks whenever control or data is transferred to/from the untrusted OS. The enclave-OS interface on SGX, however, can be extremely large if we wish to run existing unmodified binaries inside enclaves. This paper presents Ratel, a dynamic binary translation engine running inside SGX enclaves on Linux. Ratel offers complete interposition, the ability to interpose on all executed instructions in the enclave and monitor all interactions with the OS. Instruction-level interposition offers a general foundation for implementing a large variety of inline security monitors in the future. We take a principled approach in explaining why complete interposition on SGX is challenging. We draw attention to 5 design decisions in SGX that create fundamental trade-offs between performance and ensuring complete interposition, and we explain how to resolve them in the favor of complete interposition. To illustrate the utility of the Ratel framework, we present the first attempt to offer binary compatibility with existing software on SGX. We report that Ratel offers binary compatibility with over 200 programs we tested, including micro-benchmarks and real applications such as Linux shell utilities. Runtimes for two programming languages, namely Python and R, tested with standard benchmarks work out-of-the-box on Ratel without any specialized handling.
"Weak AI" is Likely to Never Become "Strong AI", So What is its Greatest Value for us?
Venue: arXiv cs.AI 2021
Year: 2021

AI has surpassed humans across a variety of tasks such as image classification, playing games (e.g., go, "Starcraft" and poker), and protein structure prediction. However, at the same time, AI is also bearing serious controversies. Many researchers argue that little substantial progress has been made for AI in recent decades. In this paper, the author (1) explains why controversies about AI exist; (2) discriminates two paradigms of AI research, termed "weak AI" and "strong AI" (a.k.a. artificial general intelligence); (3) clarifies how to judge which paradigm a research work should be classified into; (4) discusses what is the greatest value of "weak AI" if it has no chance to develop into "strong AI".
Classifying Video based on Automatic Content Detection Overview
Venue: arXiv cs.CV 2021
Year: 2021

Video classification and analysis is always a popular and challenging field in computer vision. It is more than just simple image classification due to the correlation with respect to the semantic contents of subsequent frames brings difficulties for video analysis. In this literature review, we summarized some state-of-the-art methods for multi-label video classification. Our goal is first to experimentally research the current widely used architectures, and then to develop a method to deal with the sequential data of frames and perform multi-label classification based on automatic content detection of video.
FixNorm: Dissecting Weight Decay for Training Deep Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Weight decay is a widely used technique for training Deep Neural Networks(DNN). It greatly affects generalization performance but the underlying mechanisms are not fully understood. Recent works show that for layers followed by normalizations, weight decay mainly affects the effective learning rate. However, despite normalizations have been extensively adopted in modern DNNs, layers such as the final fully-connected layer do not satisfy this precondition. For these layers, the effects of weight decay are still unclear. In this paper, we comprehensively investigate the mechanisms of weight decay and find that except for influencing effective learning rate, weight decay has another distinct mechanism that is equally important: affecting generalization performance by controlling cross-boundary risk. These two mechanisms together give a more comprehensive explanation for the effects of weight decay. Based on this discovery, we propose a new training method called FixNorm, which discards weight decay and directly controls the two mechanisms. We also propose a simple yet effective method to tune hyperparameters of FixNorm, which can find near-optimal solutions in a few trials. On ImageNet classification task, training EfficientNet-B0 with FixNorm achieves 77.7%, which outperforms the original baseline by a clear margin. Surprisingly, when scaling MobileNetV2 to the same FLOPS and applying the same tricks with EfficientNet-B0, training with FixNorm achieves 77.4%, which is only 0.3% lower. A series of SOTA results show the importance of well-tuned training procedures, and further verify the effectiveness of our approach. We set up more well-tuned baselines using FixNorm, to facilitate fair comparisons in the community.
Adversarial Specification Mining
Venue: arXiv cs.SE 2021
Year: 2021

There have been numerous studies on mining temporal specifications from execution traces. These approaches learn finite-state automata (FSA) from execution traces when running tests. To learn accurate specifications of a software system, many tests are required. Existing approaches generalize from a limited number of traces or use simple test generation strategies. Unfortunately, these strategies may not exercise uncommon usage patterns of a software system. To address this problem, we propose a new approach, adversarial specification mining, and develop a prototype, DICE (Diversity through Counter-Examples). DICE has two components: DICE-Tester and DICE-Miner. After mining Linear Temporal Logic specifications from an input test suite, DICE-Tester adversarially guides test generation, searching for counterexamples to these specifications to invalidate spurious properties. These counterexamples represent gaps in the diversity of the input test suite. This process produces execution traces of usage patterns that were unrepresented in the input test suite. Next, we propose a new specification inference algorithm, DICE-Miner, to infer FSAs using the traces, guided by the temporal specifications. We find that the inferred specifications are of higher quality than those produced by existing state-of-the-art specification miners. Finally, we use the FSAs in a fuzzer for servers of stateful protocols, increasing its coverage.