Analysis of the Causes, the Influential Aspects and the Risk of Sneaker Speculation and Suggestion

Sneaker speculation is one of the most attractive economic phenomena.The purpose of the paper is to avoid too many consumers blindly following the trend to join the sneaker speculation and eventually causes financial lost due to the sneaker speculation trend being too hot.This study is one of the first few attempts to analyze every aspect of sneaker speculation and give youth suggestion on purchasing sneakers.First,this paper analyzed the cause and influential aspect of sneaker speculation,and made a preliminary analysis for readers who first enter the sneaker market.Second,the risk of sneaker speculation was analyzed.Then,suggestions for the youth to establish correct values and consumption concept were given.Finally,the conclusion suggested that the price of sneakers is too high over their value,consumers should buy sneaker rationally.Hopefully,consumers can establish a more rational consumption concept for consumers.More powerfully regulated sneaker market is necessary,so that the price of sneakers is more reasonable and affordable for consumers.

Secure Speculation via Speculative Secret Flow Tracking

Speculative execution attacks can leak arbitrary program data under malicious speculation,presenting a severe security threat.Based on two key observations,this paper presents a software-transparent defense mechanism called speculative secret flow tracking(SSFT),which is capable of defending against all cache-based speculative execution attacks with a low performance overhead.First,we observe that the attacker must use array or pointer variables in the victim code to access arbitrary memory data.Therefore,we propose a strict definition of secret data to reduce the amount of data to be protected.Second,if the load is not data-dependent and control-dependent on secrets,its speculative execution will not leak any secrets.Thus,this paper introduces the concept of speculative secret flow to analyze how secret data are obtained and propagated during speculative execution.By tracking speculative secret flow in hardware,SSFT can identify all unsafe speculative loads(USLs)that are dependent on secrets.Moreover,SSFT exploits three different methods to constrain USLs’speculative execution and prevent them from leaking secrets into the cache and translation lookaside buffer(TLB)states.This paper evaluates the performance of SSFT on the SPEC CPU 2006 workloads,and the results show that SSFT is effective and its performance overhead is very low.To defend against all speculative execution attack variants,SSFT only incurs an average slowdown of 4.5%(Delay USL-L1Miss)or 3.8%(Invisible USLs)compared to a non-secure processor.Our analysis also shows that SSFT maintains a low hardware overhead.

Optimization Strategies Oriented to Loop Characteristics in Software Thread Level Speculation Systems

Thread level speculation provides not only a simple parallel programming model, but also an effective mech- anism for thread-level parallelism exploitation. The performance of software speculative parallel models is limited by high global overheads caused by different types of loops. These loops usually have different characteristics of dependencies and different requirements of optimization strategies. In this paper, we propose three comprehensive optimization techniques to reduce different factors of global overheads, aiming at requirements from different types of loops. Inter-thread fetching can reduce the high mis-speculation rate of the loops with frequent dependencies and out-of-order committing can reduce the control overhead of the loops with infrequent dependencies, while enhanced dynamic task granularity resizing can reduce the control overhead and optimize the global overhead of the loops with changing characteristics of dependencies. All these three optimization techniques have been implemented in HEUSPEC~ a software TLS system. Experimental results indicate that they can satisfy tile demands from different groups of benchmarks. The combination of these techniques can improve the performance of all benchmarks and reach a higher average speedup.

A TLP approach for BGP based on local speculation

With the explosive growth of Internet, the low efficiency of BGP has seriously influenced its usability. In this work, a TLP （thread level parallelism） approach with local speculation is proposed to improve the BGP performance. The thread partition is locally performed on each separated sub-module at route processing, and the speculation strategy is implemented to guarantee the memory consistency and sequential commit. Experiments on Intel Quad-core server show that this approach reaches an average speedup of 1.46 under single peer, multi-peers and route flapping. It is also shown that the packet throughput can be improved greatly under multiple sessions by taking advantage of TLP.

Research on Chinese negation and speculation： corpus annotation and identification

Identifying negative or speculative narrative frag- ments from facts is crucial for deep understanding on natu- ral language processing （NLP）. In this paper, we firstly con- struct a Chinese corpus which consists of three sub-corpora from different resources. We also present a general framework for Chinese negation and speculation identification. In our method, first, we propose a feature-based sequence labeling model to detect the negative or speculative cues. In addition, a cross-lingual cue expansion strategy is proposed to increase the coverage in cue detection. On this basis, this paper presents a new syntactic structure-based framework to identify the linguistic scope of a negative or speculative cue, instead of the traditional chunking-based framework. Experimental results justify the usefulness of our Chinese corpus and the appropriateness of our syntactic structure-based framework which has showed significant improvement over the state-of-the-art on Chinese negation and speculation identification.

Housing Market Speculation and Firm Productivity:Evidence from China

An appreciation in the price of housing could generate speculation opportunities for enterprises.Many firms try to seize these opportunities for quick returns.This may harm their productivity.We use the real estate boom and the investment activities of Chinese manufacturing firms during 2006–2017 to examine the processes and the net effect of housing market speculation on firm productivity.We find that housing market speculation negatively affects firm productivity by crowding out investments in research and development and productive fixed assets,and the positive effect brought about by a strengthened balance sheet and better financing capacity is outweighed by the crowding-out effect.This negative impact is more evident in state-owned firms,but is mitigated in regions and periods with better business environments.Set in the overheated housing market in China,our study contributes to the existing literature by exploring how firms’investments outside of their main business influence firm behavior and productivity.

Say No to Property Speculation

China’s property speculators are worried as house prices lose their ascendance momentum due to government intervention. It seems impossible to rake in the huge profi ts of the past years by buying low and selling high. Some jittery speculators are no longer thinking about how much profi t they can make.

Return to Reality:Speculative Design of Interaction Ritual Chains and Exploration of Alone Together

“Alone together”is an ever-changing“wicked problem.”In this research and practice,the author tries to combine experience design,referring to the theory of“interaction ritual chain theory,”and creatively add interactive experience to discursive works,so that the audience can think between the real and the virtual.

Effects of intervention policies on speculation in housing market:Evidence from China

The real estate market in China is often subject to government intervention while it is widely believed that speculation is one of the driving forces for the dramatic increase in house price.By controlling macroeconomic factors,we detect two types of speculative traders,fundamentalists and chartists,respectively.Fundamentalists rely on fundamental value for trading while chartists hold a momentum expectation.We further assess the effects of government intervention policies on house price and speculation.The tightening policy cools down while the stimulating policy enhances the speculation.The contrasting effects of different policies on speculations indicate the challenge in handling the Chinese housing market.

SBFT:A BFT Consensus Mechanism Based on DQN Algorithm for Industrial Internet of Thing

With the development and widespread use of blockchain in recent years,many projects have introduced blockchain technology to solve the growing security issues of the Industrial Internet of Things(IIoT).However,due to the conflict between the operational performance and security of the blockchain system and the compatibility issues with a large number of IIoT devices running together,the mainstream blockchain system cannot be applied to IIoT scenarios.In order to solve these problems,this paper proposes SBFT(Speculative Byzantine Consensus Protocol),a flexible and scalable blockchain consensus mechanism for the Industrial Internet of Things.SBFT has a consensus process based on speculation,improving the throughput and consensus speed of blockchain systems and reducing communication overhead.In order to improve the compatibility and scalability of the blockchain system,we select some nodes to participate in the consensus,and these nodes have better performance in the network.Since multiple properties determine node performance,we abstract the node selection problem as a joint optimization problem and use Dueling Deep Q Learning(DQL)to solve it.Finally,we evaluate the performance of the scheme through simulation,and the simulation results prove the superiority of our scheme.

Read-Write Dependency Aware Register Allocation

Read-write dependency is an important factor restricting software efficiency.Timing Speculative(TS)is a processing architecture aiming to improve energy efficiency of microprocessors.Timing error rate,influenced by the read-write dependency,bottlenecks the voltage down-scaling and so the energy efficiency of TS processors.We proposed a method called Read-Write Dependency Aware Register Allocation.It is based on the Read-Write Dependency aware Interference Graph(RWDIG)conception.Registers are reallocated to loosen the read-write dependencies,so resulting in a reduction of timing errors.The traditional no operation(Nop)padding method is also redesigned to increase the distance value to above 2.We analyzed the dependencies of registers and maximized the average distance value of read and write dependencies.Experimental results showed that we can reduce all read-write dependency by Nop padding,as well as the overhead timing errors.An energy saving of approximately 7%was achieved.

基于信誉的动态授权PBFT共识机制

区块链是一种基于零信任基础、去中心化及不可篡改的分布式账本技术。共识算法作为区块链主要技术之一,其效率直接影响区块链系统性能。针对PBFT共识算法运行效率低的问题,本文提出了基于信誉的动态授权PBFT共识机制,引入信誉评价体系对系统节点进行信誉评价,动态决定从信誉最高的节点中选取共识节点,同时实现了非停机情况下动态增删节点的功能,且随着系统长期运行,所能容忍的拜占庭节点动态增加;优化了一致性协议,将传统的一致性协议与基于speculation技术的拜占庭协议进行融合,降低了算力开销和通信代价;通过对共识节点的信誉及行为分析,进一步降低恶意节点成为共识节点的概率,解决了由拜占庭节点作为主节点带来的交易延迟增加问题。最后从算力开销、交易吞吐量和容错性能等方面进行了论证分析。

DS-PBFT:一种基于距离的面向区块链的共识算法

共识算法作为区块链的核心技术,决定了区块链系统的性能.其中,PBFT是最具有代表性的一种共识算法,但它存在以下缺点:通信代价大、共识时延长.由此诞生了许多通过各种方法减小共识节点规模来提升PBFT效率的一类算法,但是它们都不是基于距离因素的,并且具有与PBFT类似的缺点.基于此,本文提出了一种基于距离的面向区块链的共识算法.首先通过Grouping算法对节点进行分组,将距离较近的节点分成一组进行共识,从而在缩短共识节点之间距离的基础上减少共识时延.同时结合speculation技术,降低节点间通信的时间复杂度,从而减少了系统的通信代价.最后理论分析了影响共识时延和通信代价的两个指标:节点间的平均延迟、通信次数,证明了DS-PBFT在共识时延以及通信代价这两个方面均要优于PBFT和现有的这一类算法;通过实验验证了DS-PBFT相比PBFT和现有的这一类算法,降低了共识时延.

Policy Recommendations to Prevent Financial Risks Caused by Speculative Capital Flows

There are two inaccuracies to the common belief that hot money flows into China through international trade: "hot money" and "trade channels". First, China’s domestic financial market is still at the initial stage of development, and foreign exchange control has put barriers to entry and exit of overseas capital. Strictly speaking, the short-term highly liquid "hot money" does not exist in China, and is referred to as long-term speculative capital in this paper. Second, such capital does not come from trade alone. International trade is only part of corporate business operations, and a more reasonable and complete description is that "overseas capital flows into China through foreign-funded enterprises". Through analysis of the inflow channels, this paper estimates that the stock of long-term speculative capital between 1999 and 2006 stands at almost US$ 400 billion. The year 2006 saw an increase of US$ 98.09 billion, of which US$ 35.4 billion was formed through international trade, which equals to roughly 20% of trade surplus that year. But this does not mean that 20% of trade surplus is long-term speculative capital.

Commodity Market and Financial Derivative Instruments: Is There a Cointegration?

Over the past decade, agricultural commodity prices underwent wild swings, Periods of high growth in 2008 were followed by periods of sudden decrease in 2009, The excessive increase in food price volatility is a subject of great interest because it has to do with the survival of mankind, Recent academic studies and policy makers have reached sharply different conclusions about the dynamics of such fluctuations, Also, some of them have indicated the main cause in speculative transactions and in the little regulation of futures markets, This paper intends to verify if there exists a co-implication between wheat futures price and some financial “speculative” variables, The results of cointegration analysis show that there is no evidence that financial derivative instruments determine the fluctuations of wheat futures price.

An Optimized Resource Scheduling Strategy for Hadoop Speculative Execution Based on Non-cooperative Game Schemes

Hadoop is a well-known parallel computing system for distributed computing and large-scale data processes.“Straggling”tasks,however,have a serious impact on task allocation and scheduling in a Hadoop system.Speculative Execution(SE)is an efficient method of processing“Straggling”Tasks by monitoring real-time running status of tasks and then selectively backing up“Stragglers”in another node to increase the chance to complete the entire mission early.Present speculative execution strategies meet challenges on misjudgement of“Straggling”tasks and improper selection of backup nodes,which leads to inefficient implementation of speculative executive processes.This paper has proposed an Optimized Resource Scheduling strategy for Speculative Execution(ORSE)by introducing non-cooperative game schemes.The ORSE transforms the resource scheduling of backup tasks into a multi-party non-cooperative game problem,where the tasks are regarded as game participants,whilst total task execution time of the entire cluster as the utility function.In that case,the most benefit strategy can be implemented in each computing node when the game reaches a Nash equilibrium point,i.e.,the final resource scheduling scheme to be obtained.The strategy has been implemented in Hadoop-2.x.Experimental results depict that the ORSE can maintain the efficiency of speculative executive processes and improve fault-tolerant and computation performance under the circumstances of Normal Load,Busy Load and Busy Load with Skewed Data.

Timing Error Aware Register Allocation in TS

Timing speculative(TS)architecture is promising for improving the energy efficiency of microprocessors.Error recovery units,designed for tolerating occasional timing errors,have been used to support a wider range of voltage scaling,therefore to achieve a better energy efficiency.More specifically,the timing error rate,influenced mainly by data forwarding,is the bottleneck for voltage down-scaling in TS processors.In this paper,a new Timing Error Aware Register Allocation method is proposed.First,we designed the Dependency aware Interference Graph(DIG)construction to get the information of Read after Write(RAW)in programs.To build the construction,we get the disassemble code as input and suppose that there are unlimited registers,the same way as so-called virtual registers in many compilers.Then we change the disassemble codes to the SSA form for each basic block to make sure the registers are defined only once.Based on the DIG construction,registers were real-located to eliminate the timing error,by loosening the RAW dependencies.We con-struct the DIG for each function of the program and sort the edge of DIG by an increasing weight order.Since a smaller weighted-edge value means that its owner nodes have more frequent access in instruction flows,we expect it in different registers with no read-write dependency.At the same time,we make sure that there are no additional new spill codes emerging in our algorithm to minimize the rate of spill code.A high rate of spill code will not only decrease the performance of the system but also increase the unexpected read-write dependency.Next,we reallocate the reg-isters by weight order in turn to loosen the RAW dependencies.Furthermore,we use the NOP operation to pad the instructions with a minimal distance value of 2.Experiment results showed that the average distance of RAW dependencies was increased by over 20%.

Automatic Detection of Stellate Lesions in Digital Mammograms Using Multi-scale SIFT

This paper presents a novel automatic mammography recognition approach used to develop computer-aided diagnostic systems that require a robust method to assist the radiologist in identifying and recognizing speculations from a multitude of lines corresponding to the normal fibrous breast tissue.Following this rationale,this paper introduces a novel approach for detecting the speculated lesions in digital mammograms based on multi-scale SIFT(scale-invariant feature transform)orientations.The proposed method starts by estimating a set of key points that best represent the image mammography in a scale space.We then benefit from SIFT algorithm to locally characterize each key point by assigning a consistent orientation.Thereafter,a set of three features are extracted for each pixel in the image mammogram based on these orientations.The extracted features are fed into BDT(binary decision tree)in order to perform per pixel classification and decide whether the pixel is normal or abnormal.We evaluate the proposed system on BCDR(breast cancer digital repository)database and the experimental results show that our method is accurate with 97.95%recognition rate,while it is robust to illumination changes,rotation and scale variations.

Preliminary Discussion on Application of “3W” Teaching Method in Architectural Design Course

Architectural design course is the main course of architecture major.“Master-apprentice” guidance is the main mode of architectural design course teaching.How to teach between teachers and apprentices? Architectural design teachers in different colleges and universities do not have a unified approach,which easily leads to confusion in teaching.This study put forward “ 3W ” teaching method,and made a preliminary discussion on its concept,structure and application.“ 3W ” method is a method that the architectural design instructor uses three progressive questions to guide the whole process of architectural design.Through a detailed discussion of the three questions,it showed that this method was a building design guiding method with simple operation,easy grasp and adaptability.

A New Speculative Execution Algorithm Based on C4.5 Decision Tree for Hadoop

As a distributed computing platform, Hadoop provides an effective way to handle big data. In Hadoop, the completion time of job will be delayed by a straggler. Although the definitive cause of the straggler is hard to detect, speculative execution is usually used for dealing with this problem, by simply backing up those stragglers on alternative nodes. In this paper, we design a new Speculative Execution algorithm based on C4.5 Decision Tree, SECDT, for Hadoop. In SECDT, we speculate completion time of stragglers and also of backup tasks, based on a kind of decision tree method: C4.5 decision tree. After we speculate the completion time, we compare the completion time of stragglers and of the backup tasks, calculating their differential value, and selecting the straggler with the maximum differential value to start the backup task.Experiment result shows that the SECDT can predict execution time more accurately than other speculative execution methods, hence reduce the job completion time.