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This paper presents the process of organizing and hosting the National Online Contest in Informatics dedicated to the memory of Mladen Manev. This description includes some technical and scientific aspects of the contest management system and the competition tasks. The presented analysis of the collected data during the competition such as participation statistics, problem difficulty, results and system workload is used to assess to what extent the aims of the competition have been successfully achieved. [ABSTRACT FROM AUTHOR]

In recent years, with the development of computer science, deep learning is held as competent enough to solve the problem of inference and learning in high dimensional space. Therefore, it has received unprecedented attention from both the academia and the business community. Compared with CPU/GPU, FPGA has attracted much attention for its high-energy efficiency, short development cycle and reconfigurability in the aspect of deep learning algorithm. However, because of the limited research on OpenCL optimization on FPGA of deep learning algorithms, OpenCL tools and models applied to CPU/GPU cannot be directly used on FPGA. This makes it difficult for software programmers to use FPGA when implementing deep learning algorithms for a rewarding performance. To solve this problem, this paper proposed an OpenCL computational model based on FPGA template architecture to optimize the time-consuming convolution layer in deep learning. The comparison between the program applying the computational model and the corresponding optimization program provided by Xilinx indicates that the former is 8-40 times higher than the latter in terms of performance. [ABSTRACT FROM AUTHOR]

This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics. [ABSTRACT FROM AUTHOR]

Error correction codes (ECCs) are commonly used to deal with soft errors in memory applications. Typically, Single Error Correction-Double Error Detection (SEC-DED) codes are widely used due to their simplicity. However, the phenomenon of more than one error in the memory cells has become more serious in advanced technologies. Single Error Correction-Double Adjacent Error Correction (SEC-DAEC) codes are a good choice to protect memories against double adjacent errors that are a major multiple error pattern. An important consideration is that the ECC encoder and decoder circuits can also be affected by soft errors, which will corrupt the memory data. In this paper, a method to design fault tolerant encoders for SEC-DAEC codes is proposed. It is based on the fact that soft errors in the encoder have a similar effect to soft errors in a memory word and achieved by using logic sharing blocks for every two adjacent parity bits. In the proposed scheme, one soft error in the encoder can cause at most two errors on adjacent parity bits, thus the correctness of memory data can be ensured because those errors are correctable by the SEC-DAEC code. The proposed scheme has been implemented and the results show that it requires less circuit area and power than the encoders protected by the existing methods. [ABSTRACT FROM AUTHOR]

Historically, LCD cyclic codes were referred to as reversible cyclic codes, which had applications in data storage. Due to a newly discovered application in cryptography, there has been renewed interest in LCD codes. In this paper, we explore two special families of LCD cyclic codes, which are both BCH codes. The dimensions and the minimum distances of these LCD BCH codes are investigated. [ABSTRACT FROM AUTHOR]

• Fileless malware has no signature because it does not leverage executable files. • We suggest a memory-based approach for detecting and analyzing fileless malware. • This proposed method offers useful insight for the experts working in this field. • The proposed methodʼs applicability was demonstrated using a real case study sample. The rapid advancements in cyber-attack strategies are in parallel with the measures for detection, analysis, and prevention. Attackers have recently developed fileless malware that can simply bypass existing security mechanisms. Researchers publish reports to help discover fileless malware and to better understand the threatʼs scope to counteract it. However, with the lack of studies on fileless malware regarding the classification and the scale of the threat, they have not been thoroughly analyzed. As a result, in this research, we explored the most recent advancements in fileless malware prevention and detection and highlighted future research challenges. We also propose an analytical approach based on the attack strategies and attributes of the selected sample. Our method simplifies feature extraction and reduces processing load. Furthermore, compared to the static analysis we do not need for decompression and unpacking for the analysis. We applied the proposed method on a real case example. It has been seen that information about fileless malware detection, working mechanism, attack method and attacker named "Kovter" can be accessed. Our approach is advantageous and can be applied as a new technique for fileless malware detection to protect systems from cyber threats. This paper also presents an insight to the fileless malware threat and provides a basic review of the methods and techniques used in the detection and analysis of fileless malware attacks. [ABSTRACT FROM AUTHOR]

In the first section of the paper I present Alan Turing’s notion of effective memory, as it appears in his 1936 paper ‘On Computable Numbers, With an Application to The Entscheidungsproblem’. This notion stands in surprising contrast with the way memory is usually thought of in the context of contemporary computer science. Turing’s view (in 1936) is that for a computing machine to remember a previously scanned string of symbols is not to store an internal symbolic image of this string. Rather, memory consists in the fact that the past scanning of the string affects the behavior of the computer in the face of potential future inputs. In the second, central section of the paper I begin exploring how this view of Turing’s bears upon contemporary discussions in the philosophy of mind. In particular, I argue that Turing’s approach can be used to lend support to dispositional conceptions of the propositional attitudes, like the one recently presented by Matthews (2007), and that his effective memory manifests some of the characteristics of Millikan’s (1996) pushmepullyou mental states. [ABSTRACT FROM AUTHOR]

Original Hebbian encoding scheme of bidirectional associative memory (BAM) provides a poor pattern capacity and recall performance. Based on Rosenblatt's perceptron learning algorithm, the pattern capacity of BAM is enlarged, and perfect recall of all training pattern pairs is guaranteed. However, these methods put their emphases on pattern capacity, rather than error correction capability which is another critical point of BAM. This paper analyzes the sensitivity to noise in RAM and obtains an interesting idea to improve noise immunity of BAM. Some researchers have found that the noise sensitivity of BAM relates to the minimum absolute value of net inputs (MAV). However, in this paper, the analysis on failure association shows that it is related not only to MAV but also to the variance of weights associated with synapse connections. In fact, it is a positive monotone increasing function of the quotient of MAV divided by the variance of weights. This idea provides an useful principle of improving error correction capability of RAM. Some revised encoding schemes, such as small variance learning for RAM (SVBAM), evolutionary pseudorelaxation learning for BAM (EPRLAB) and evolutionary bidirectional learning (EBL), have been introduced to illustrate the performance of this principle. All these methods perform better than their original versions in noise immunity. Moreover, these methods have no negative effect on the pattern capacity of BAM. The convergence of these methods is also discussed in this paper. If there exist solutions, EPRLAB and EBL always converge to a global optimal solution in the senses of both, pattern capacity and noise immunity. However, the convergence of SVBAM may be affected by a preset function. [ABSTRACT FROM AUTHOR]

Scratchpad memories in GPU architectures are employed as software-controlled caches to increase the effective GPU memory bandwidth. Through the use of well-known optimization techniques, such as privatization and tiling, they are properly exploited. Typically, they are banked memories which are addressed with a \textmod(2^N) bank indexing scheme. Although their bandwidth is fully exploited for linear memory accesses, their performance is burdened when non-unit strides appear in memory access patterns because they provoke bank conflicts. This paper explores the use of configurable bit-vector and bitwise XOR-based hash functions to evenly distribute memory addresses of the access patterns over the memory banks, reducing the number of bank conflicts. An exhaustive, but lightweight, search is used to configure bit-vector hash functions. Bitwise hash functions are configured with heuristics. Hardware and software implementations are carried out. For the hardware approach, the experimental results show 24 percent performance speed-up for 22 benchmarks on GPGPU-Sim, a Fermi-like simulator. Bank conflicts are reduced by 96 percent with bit-vector hash functions, and 97 percent with bitwise hash functions using our proposed Minimum Imbalance Heuristic. The software approach, using bit-vector hash functions, demonstrates 23 percent speed-up and 96 percent bank conflict reduction on a Fermi GPU, and 33 percent speed-up and 99 percent bank conflict reduction on a Kepler GPU. [ABSTRACT FROM AUTHOR]

First-Come-First-Served (FCFS) mutual exclusion (ME) is the problem of ensuring that processes attempting to concurrently access a shared resource do so one by one, in a fair order. In this paper, we close the complexity gap between FCFS ME and ME in the asynchronous shared memory model where processes communicate using atomic reads and writes only, and do not fail. Our main result is the first known FCFS ME algorithm that makes O(log N) remote memory references (RMRs) per passage and uses only atomic reads and writes. Our algorithm is also adaptive to point contention. More precisely, the number of RMRs a process makes per passage in our algorithm is Θ(min( k, log N)), where k is the point contention. Our algorithm matches known RMR complexity lower bounds for the class of ME algorithms that use reads and writes only, and beats the RMR complexity of prior algorithms in this class that have the FCFS property. [ABSTRACT FROM AUTHOR]

The leader election problem is unsolvable for some anonymous networks. A leader election algorithm for anonymous networks thus elects a leader whenever it is possible; if it is impossible, the algorithm reports this fact. This paper investigates the space complexity of the leader election problem in anonymous networks, where the space complexity is measured by the size (in the number of bits) of memory per processor used by a leader election algorithm. We first observe that Ω(M + log d) bits are necessary and then show that O(n log d) bits are sufficient to construct a leader election algorithm that works on any network, where n, d and M are the number of processors, the maximum number of adjacent processors, and the maximum size (in bits) of a message, respectively. We next show that, for any arbitrarily fixed constant n, O(1) bits are sufficient to construct a leader election algorithm that works in any network of size n. [ABSTRACT FROM AUTHOR]

This paper presents a scalable method for parallel symbolic on-the-fly model checking in a distributed memory environment. Our method combines a scheme for on-the-fly model checking for safety properties with a scheme for scalable reachability analysis. We suggest an efficient, BDD-based algorithm for a distributed construction of a counterexample. The extra memory requirement for counterexample generation is evenly distributed among the processes by a memory balancing procedure. At no point during computation does the memory of a single process contain all the data. This enhances scalability. Collaboration between the parallel processes during counterexample generation reduces memory utilization for the backward step. We implemented our method on a standard, loosely-connected environment of workstations, using a high-performance model checker. Our initial performance evaluation, carried out on several large circuits, shows that our method can check models that are too large to fit in the memory of a single node. Our on-the-fly approach may find counterexamples even when the model is too large to fit in the memory of the parallel system. [ABSTRACT FROM AUTHOR]