Reviewerly/exHarmony huggingface dataset:
BinTrimmer: Towards Static Binary Debloating Through Abstract Interpretation

The increasing complexity of modern programs motivates software engineers to often rely on the support of third-party libraries. Although this practice allows application developers to achieve a compelling time-to-market, it often makes the final product bloated with conspicuous chunks of unused code. Other than making a program unnecessarily large, this dormant code could be leveraged by willful attackers to harm users. As a consequence, several techniques have been recently proposed to perform program debloating and remove (or secure) dead code from applications. However, state-of-the-art approaches are either based on unsound strategies, thus producing unreliable results, or pose too strict assumptions on the program itself. In this work, we propose a novel abstract domain, called Signedness-Agnostic Strided Interval, which we use as the cornerstone to design a novel and sound static technique, based on abstract interpretation, to reliably perform program debloating. Throughout the paper, we detail the specifics of our approach and show its effectiveness and usefulness by implementing it in a tool, called BinTrimmer, to perform static program debloating on binaries. Our evaluation shows that BinTrimmer can remove up to 65.6% of a library’s code and that our domain is, on average, 98% more precise than the related work.
PS2

Most of the data is extracted and processed by Spark in Tencent Machine Learning Platform. However, seldom of them use Spark MLlib, an official machine learning (ML) library on top of Spark due to its inefficiency. In contrast, systems like parameter servers, XGBoost and TensorFlow are more used, which incur expensive cost of transferring data in and out of Spark ecosystem. In this paper, we identify the causes of inefficiency in Spark MLlib and solve the problem by building parameter servers on top of Spark. We propose PS2, a parameter server architecture that integrates Spark without hacking the core of Spark. With PS2, we leverage the power of Spark for data processing and ML training, and parameter servers for maintaining ML models. By carefully analyzing Tencent ML workloads, we figure out a widely existing computation pattern for ML models---element-wise operations among multiple high dimensional vectors. Based on this observation, we propose a new data abstraction, called Dimension Co-located Vector (DCV) for efficient model management in PS2. A DCV is a distributed vector that considers locality in parameter servers and enables efficient computation with multiple co-located distributed vectors. For ease-of-use, we also design a wide variety of advanced operators for operating DCVs. Finally, we carefully implement the PS2 system and evaluate it against existing systems on both public and Tencent workloads. Empirical results demonstrate that PS2 can outperform Spark MLlib by up to 55.6X and specialized ML systems like Petuum by up to 3.7X.
Distributed Solution of Large-Scale Linear Systems via Accelerated Projection-Based Consensus

Solving a large-scale system of linear equations is a key step at the heart of many algorithms in scientific computing, machine learning, and beyond. When the problem dimension is large, computational and/or memory constraints make it desirable, or even necessary, to perform the task in a distributed fashion. In this paper, we consider a common scenario in which a taskmaster intends to solve a large-scale system of linear equations by distributing subsets of the equations among a number of computing machines/cores. We propose a new algorithm called Accelerated Projection-based Consensus, in which at each iteration every machine updates its solution by adding a scaled version of the projection of an error signal onto the nullspace of its system of equations, and the taskmaster conducts an averaging over the solutions with momentum. The convergence behavior of the proposed algorithm is analyzed in detail and analytically shown to compare favorably with the convergence rate of alternative distributed methods, namely distributed gradient descent, distributed versions of Nesterov's accelerated gradient descent and heavy-ball method, the block Cimmino method, and Alternating Direction Method of Multipliers. On randomly chosen linear systems, as well as on real-world data sets, the proposed method offers significant speed-up relative to all the aforementioned methods. Finally, our analysis suggests a novel variation of the distributed heavy-ball method, which employs a particular distributed preconditioning and achieves the same theoretical convergence rate as that in the proposed consensus-based method.
Efficient Logspace Classes for Enumeration, Counting, and Uniform Generation

In this work, we study two simple yet general complexity classes, based on logspace Turing machines, which provide a unifying framework for efficient query evaluation in areas like information extraction and graph databases, among others. We investigate the complexity of three fundamental algorithmic problems for these classes: enumeration, counting and uniform generation of solutions, and show that they have several desirable properties in this respect. Both complexity classes are defined in terms of nondeterministic logspace transducers (NL transducers). For the first class, we consider the case of unambiguous NL transducers, and we prove constant delay enumeration, and both counting and uniform generation of solutions in polynomial time. For the second class, we consider unrestricted NL transducers, and we obtain polynomial delay enumeration, approximate counting in polynomial time, and polynomial-time randomized algorithms for uniform generation. More specifically, we show that each problem in this second class admits a fully polynomial-time randomized approximation scheme (FPRAS) and a polynomial-time Las Vegas algorithm for uniform generation. Interestingly, the key idea to prove these results is to show that the fundamental problem #NFA admits an FPRAS, where #NFA is the problem of counting the number of strings of length n accepted by a nondeterministic finite automaton (NFA). While this problem is known to be #P-complete and, more precisely, SpanL-complete, it was open whether this problem admits an FPRAS. In this work, we solve this open problem, and obtain as a welcome corollary that every function in SpanL admits an FPRAS.
Analysis of attribute‐based cryptographic techniques and their application to protect cloud services

Abstract Recent technological advances such as the internet of things, fog computing, and cloud applications lead to exponential growth in the amount of generated data. Indeed, cloud storage services have experienced unprecedented usage demand. The loss of user control over their cloud stored data introduced several security and privacy concerns. To address these concerns, cryptographic techniques are widely adopted at the user side. Attribute‐based cryptography is commonly used to provide encrypted and/or authenticated access to outsourced data in remote servers. However, the use of these cryptographic mechanisms often increase the storage and computation costs; consequently, the energy consumption in the entire cloud ecosystem. In this paper, we provide a comparative analysis of different attribute‐based cryptographic mechanisms suitable for cloud data sharing services. We also provide a detailed discussion of different reviewed schemes, w.r.t. supported features, namely, security, privacy, and functional requirements. In addition, we explore the limitations of existing attribute‐based cryptographic mechanisms and propose future research directions to better fit the growing needs of this cloud environment in terms of energy savings, processing and storage efficiency, and availability requirements.
Mycelium Artifacts

We present our work with mycelium-the vegetative part of a fungus made up of fine filaments that can be grown on a substrate to take a specific form-as a sustainable and accessible biofabrication material for DIS. Over the course of one year, our interdisciplinary team experimented with growing diverse 3D forms out of mycelium. Drawing on examples from our hands-on work, our DIS demo will present three fabrication possibilities of mycelium 1) disposable low fidelity enclosures for rapid prototypes; 2) mycelium forms sculpted with everyday materials; and 3) physical replicas of 3D models grown out of mycelium. We also describe challenges and workarounds for adopting mycelium into HCI workflows based on the obstacles we encountered.
Towards Efficient Deep Neural Network Training by FPGA-Based Batch-Level Parallelism

Training Deep Neural Networks (DNNs) requires a significant amount of time and resources to obtain acceptable results, which severely limits its deployment in resource-limited platforms. This paper proposes DarkFPGA, a novel customizable framework to efficiently accelerate the entire DNN training on a single FPGA platform. First, we explore batch-level parallelism to enable efficient training on FPGAs. Second, we devise a novel hardware architecture optimised by a batch-oriented data pattern and tiling techniques to effectively exploit parallelism. Moreover, an analytical model is developed to determine the optimal design parameters for the DarkFPGA accelerator with respect to a specific network specification and FPGA resource constraints. Our results show that the accelerator is able to perform about 11 times faster than CPU training and about a third of the energy consumption than GPU training using 8-bit integers for training VGG-like networks on the CIFAR dataset for the Maxeler MAX5 platform.
O3BNN

Binarized Neural Networks (BNN) have drawn tremendous attention due to significantly reduced computational complexity and memory demand. They have especially shown great potential in cost- and power-restricted domains, such as IoT and smart edge-devices, where reaching a certain accuracy bar is often sufficient, and real-time is highly desired.
A dynamic multiple attribute decision making model with learning of fuzzy cognitive maps

This paper concerns with a new multiple attribute decision making (MADM) model to cope with temporal performance of alternatives during different time periods. Dynamic MADM problems has grabbed remarkable attention of decision analysis community in recent years. In parallel with the recent advances in information technologies, firms steadily recognize the importance of data, and business analytics solutions is about to become standard business practice. Majority of the dynamic MADM literature deals with combining past and present data by means of aggregation operators. There is a research gap in developing data-driven methodologies to capture the patterns and trends in the historical data, and provide decision makers with meaningful insights in decision making practices. Analogous with the fact that style of decision making evolving from intuition-based to data-driven, this study proposes a new dynamic MADM model by learning of fuzzy cognitive maps (FCMs) to support decision makers in making informed decisions by considering future performance of alternatives. According to proposed model, Jaya algorithm, a simple and effective metaheuristic optimization method, is used to train FCMs to capture the patterns in historical data. Then, short-, medium-, and long-term future decision making matrices are generated. Finally, past, current and future decision making matrices are taken into consideration, and ranking of alternatives are obtained based on closeness coefficients. The proposed model is realized in a real-life supplier performance evaluation problem.
A Novel Streaming Data Clustering Algorithm Based on Fitness Proportionate Sharing

As an unsupervised learning technique, clustering can effectively capture the patterns in a data stream based on similarities among the data.Traditional data stream clustering algorithms either heavily depend on some prior knowledge or predefined parameters while the characteristics of real-time data are considered unknown.Besides, the user-specified threshold is used to overcome the effect of outliers and noises, which significantly affects the clustering performance.The overlap among clusters is another major challenge for the existing stream clustering methods.These constraints strongly limit their real-time applications.In this paper, a two-phase stream clustering algorithm based on fitness proportionate sharing is proposed.It handles streaming data when prior knowledge is not available and maps the clustering problem into a multimodal optimization problem.It introduces a density-based objective function and adopts the fitness proportionate sharing strategy to perform a more effective search for the cluster centers.To capture the dynamic characteristics of streaming data, a recursive formula for the lower bound of the density function is derived, and a summary of historical data is established for the proposed algorithm.The proposed algorithm is applied to different data sets, and a comprehensive comparison between the proposed algorithm and five well-known data stream clustering algorithms in the literature is provided.Results show comparable or better performance relative to five popular data stream clustering algorithms.A scalability analysis of the proposed streaming clustering method is presented in this paper as well.
Semantic periodic pattern mining from spatio-temporal trajectories

Rapid development in GPS tracking techniques produces a large number of spatio-temporal trajectory data. The analysis of these data provides us with a new opportunity to discover behavioural patterns. Spatio-temporal periodic pattern mining is finding temporal regularities for interesting places. Mining periodic patterns from spatio-temporal trajectories reveals useful and important information about people’s regular and recurrent movements and behaviours. Existing periodic pattern mining algorithms suffer from two main drawbacks. They assume regularly sampled and evenly spaced trajectory data as input which is unlike real world data, traditional methods also fail to consider background aspatial information despite many applications requiring a semantic interpretation of movement behaviours. In this paper, we propose a new semantic periodic pattern mining algorithm from spatio-temporal trajectories that overcomes these two drawbacks from past studies. Experimental results with real world datasets demonstrate the efficiency and effectiveness of our proposed method.
Efficient passive measurement-device-independent quantum key distribution

The measurement-device-independent quantum key distribution (MDI-QKD) possesses the highest security among all practical quantum key distribution protocols. However, existing multi-intensity decoy-state methods may cause loopholes when modulating light intensities with practical devices. In this paper, we propose a passive decoy-state MDI-QKD protocol based on a novel structure of heralded single-photon sources. It does not need to modulate the light source into different intensities and thus can avoid leaking modulation information of decoy states to eavesdroppers, enhancing the security of practical MDI-QKD. Furthermore, by combining the passive decoy states and biased basis choices, our protocol can exhibit distinct advantages compared with state-of-the art MDI-QKD schemes even when the finite key-size effect is taken into account. Therefore, our present passive decoy-state MDI-QKD seems a promising candidate for practical implementation of quantum key distributions in the near future.
MVAN: Multi-view Attention Networks for Real Money Trading Detection in Online Games

Online gaming is a multi-billion dollar industry that entertains a large, global population. However, one unfortunate phenomenon known as real money trading harms the competition and the fun. Real money trading is an interesting economic activity used to exchange assets in a virtual world with real world currencies, leading to imbalance of game economy and inequality of wealth and opportunity. Game operation teams have been devoting much efforts on real money trading detection, however, it still remains a challenging task. To overcome the limitation from traditional methods conducted by game operation teams, we propose, MVAN, the first multi-view attention networks for detecting real money trading with multi-view data sources. We present a multi-graph attention network (MGAT) in the graph structure view, a behavior attention network (BAN) in the vertex content view, a portrait attention network (PAN) in the vertex attribute view and a data source attention network (DSAN) in the data source view. Experiments conducted on real-world game logs from a commercial NetEase MMORPG( JusticePC) show that our method consistently performs promising results compared with other competitive methods over time and verifiy the importance and rationality of attention mechanisms. MVAN is deployed to several MMORPGs in NetEase in practice and achieving remarkable performance improvement and acceleration. Our method can easily generalize to other types of related tasks in real world, such as fraud detection, drug tracking and money laundering tracking etc.
Learning a Unified Embedding for Visual Search at Pinterest

At Pinterest, we utilize image embeddings throughout our search and recommendation systems to help our users navigate through visual content by powering experiences like browsing of related content and searching for exact products for shopping. In this work we describe a multi-task deep metric learning system to learn a single unified image embedding which can be used to power our multiple visual search products. The solution we present not only allows us to train for multiple application objectives in a single deep neural network architecture, but takes advantage of correlated information in the combination of all training data from each application to generate a unified embedding that outperforms all specialized embeddings previously deployed for each product.
Steiner systems $$S(2, 4, \frac{3^m-1}{2})$$ and 2-designs from ternary linear codes of length $$\frac{3^m-1}{2}$$

Coding theory and t-designs have close connections and interesting interplay. In this paper, we first introduce a class of ternary linear codes and study their parameters. We then focus on their three-weight subcodes with a special weight distribution. We determine the weight distributions of some shortened codes and punctured codes of these three-weight subcodes. These shortened and punctured codes contain some codes that have the same parameters as the best ternary linear codes known in the database maintained by Markus Grassl at http://www.codetables.de/ . These three-weight subcodes with a special weight distribution do not satisfy the conditions of the Assmus–Mattson theorem and do not admit 2-transitive or 2-homogeneous automorphism groups in general. By employing the theory of projective geometries and projective generalized Reed–Muller codes, we prove that they still hold 2-designs. We also determine the parameters of these 2-designs. This paper mainly confirms some recent conjectures of Ding and Li regarding Steiner systems and 2-designs from a special type of ternary projective codes.
High Performance and Scalable Byzantine Fault Tolerance

With the great success of cryptocurrencies, people have paid great attention to blockchain technology and Byzantine fault tolerance (BFT) for consensus. Practical Byzantine Fault Tolerance protocol (PBFT) proposed by Castro and Liskov in 1999 is recognized as the most classic protocol for solving the Byzantine generals problem, but it still has many fatal disadvantages. Firstly, its communication complexity in the consensus process has reached O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), so that it can only be applied in a small-scale system. Secondly, it works in a completely enclosed environment, nodes are not allowed to participate or exit the network without restarting the system, making it difficult to get applied in the actual system. In order to solve these problem, this paper proposes the High Performance and Scalable Byzantine Fault Tolerance, an improved BFT protocol, called HSBFT. In normal case operation, the HSBFT's communication complexity is reduced to O(n), and it also has the ability to be scalable, making it adaptable to more diverse practical scenarios.
MPCircuits: Optimized Circuit Generation for Secure Multi-Party Computation

Secure Multi-party Computation (MPC) is one of the most influential achievements of modern cryptography: it allows evaluation of an arbitrary function on private inputs from multiple parties without revealing the inputs. A crucial step of utilizing contemporary MPC protocols is to describe the function as a Boolean circuit. While efficient solutions have been proposed for special case of two-party secure computation, the general case of more than two-party is not addressed. This paper proposes MPCircuits, the first automated solution to devise the optimized Boolean circuit representation for any MPC function using hardware synthesis tools with new customized libraries that are scalable to multiple parties. MPCircuits creates a new end-to-end tool-chain to facilitate practical scalable MPC realization. To illustrate the practicality of MPCircuits, we design and implement a set of five circuits that represent real-world MPC problems. Our benchmarks inherently have different computational and communication complexities and are good candidates to evaluate MPC protocols. We also formalize the metrics by which a given protocol can be analyzed. We provide extensive experimental evaluations for these benchmarks; two of which are the first reported solutions in multi-party settings. As our experimental results indicate, MPCircuits reduces the computation time of MPC protocols by up to 4.2×.
Sharing the LUOV: Threshold Post-quantum Signatures

We examine all of the signature submissions to Round-2 of the NIST PQC “competition” in the context of whether one can transform them into threshold signature schemes in a relatively straight forward manner. We conclude that all schemes, except the ones in the MQ family, have significant issues when one wishes to convert them using relatively generic MPC techniques. The lattice based schemes are hampered by requiring a mix of operations which are suited to both linear secret shared schemes (LSSS)-based and garbled circuits (GC)-based MPC techniques (thus requiring costly transfers between the two paradigms). The Picnic and SPHINCS+ algorithms are hampered by the need to compute a large number of hash function queries on secret data. Of the nine submissions the two which would appear to be most suitable for using in a threshold like manner are Rainbow and LUOV, with LUOV requiring less rounds and less data storage.
Performance Benchmarking and Optimization for Blockchain Systems: A Survey

Blockchain is a decentralized infrastructure widely used in emerging digital cryptocurrencies. With the fast development of blockchain technology, there are many new achievements in industry and academia. As a decentralized, shared and encrypted distributed ledger technology, blockchain has three distinctive features: decentralization, traceability, and non-tampering. Therefore, the blockchain technology has been used to implement transaction autonomy, save regulatory costs, and improve security. Its birth was even considered to be the fourth industrial revolution. However, the blockchain is still at an early stage of development, and has not been widely applied in practices. One of the main reason is the low performance issue. In order to better understand the state-of-art of the blockchain, we first introduce the architecture and consensus protocols of the current mainstream blockchain systems, then analyze some open source blockchain benchmarking tools, and summarize some blockchain systems optimization methods. Finally, we propose some suggestions for future development of blockchain systems.