Reviewerly/exHarmony huggingface dataset:
An Efficient Parallel Stochastic Gradient Descent for Matrix Factorization On GPUS

Matrix factorization (MF) is an essential method used in recommender systems, database systems, word-embedding, Graph-mining, and others. Stochastic gradient descent (SGD) is a widely-used method of solving the MF problem because it has effective accuracy in dealing with large datasets and high computing speed. SGD is hard to be parallelized as it is a sequential algorithm, but there are also some effective parallel methods proposed by researches. In this research, we propose EMF-SGD, an effective GPU-based method of large-scale recommender systems. EMF-SGD accelerated the SGD algorithm by utilizing the GPU shared-memory and warp operations. Besides, we focus on maintaining the relationship between users and items in preprocessing data to gain higher accuracy. Finally, we parallelize the EMF-SGD on multi-GPUS and proved it gains 1.8-4.3x speed up and higher accuracy over the most state-of arts algorithm GPU-MF-SGD, based on the different amount of GPUS we used.
Switchable TV-series Browsing Scheme Using Alignment of Video and Episode Plot Description

Watching TV-series is very time-consuming and it is tired to see out dozens of episodes at one time, but we always can not help doing that because of the interesting plot. In this paper, we investigate to automatically align the video with the widely existing Episode Plot Description (EPD) and design a scheme to flexibly switch TV-serial browsing between videos and EPDs with two principles: (1) optimizing the interesting content reservation of teleplay for audiences; and (2) flexibly offering convenience for quickly acquiring the main content and skimming long and boring video clips. The scheme mainly contains three components: video entities extracting and EPD parsing, video scenes and EPD sentences associating, and alignment. Video entities extracting and EPD parsing utilizes face recognition, location recognition and action learning as well as the Chinese Natural Language Processing techniques to get several video entities and EPD words named association elements. Video scenes and EPD sentences associating then use the association elements distance calculation based on ConceptNet-Zn to associate video scenes with EPD sentences. Finally, minimum cost estimate mechanism is used to align the scenes sequence to the EPD sentences and also a commendable browsing scheme is accomplished via the alignment results. Experiments conducted on a set of TV serials have demonstrated the usefulness and effectiveness of the scheme.
Multi Stage Common Vector Space for Multimodal Embeddings

Deep learning frameworks have proven to be very effective at tasks like classification, segmentation, detection, and translation. Before being processed by a deep learning model, objects are first encoded into a suitable vector representation. For example, images are typically encoded using convolutional neural networks whereas texts typically use recurrent neural networks. Similarly, other modalities of data like 3D point clouds, audio signals, and videos can be transformed into vectors using appropriate encoders. Although deep learning architectures do a good job of learning these vector representations in isolation, learning a single common representation across multiple modalities is a challenging task. In this work, we develop a Multi Stage Common Vector Space (M-CVS) that is suitable for encoding multiple modalities. The M-CVS is an efficient low-dimensional vector representation in which the contextual similarity of data is preserved across all modalities through the use of contrastive loss functions. Our vector space can perform tasks like multimodal retrieval, searching and generation, where for example, images can be retrieved from text or audio input. The addition of a new modality would generally mean resetting and training the entire network. However, we introduce a stagewise learning technique where each modality is compared to a reference modality before being projected to the M-CVS. Our method ensures that a new modality can be mapped into the MCVS without changing existing encodings, allowing the extension to any number of modalities. We build and evaluate M-CVS on the XMedia and XMedianet multimodal dataset. Extensive ablation experiments using images, text, audio, video, and 3D point cloud modalities demonstrate the complexity vs. accuracy tradeoff under a wide variety of real-world use cases.
Approximate Track Automata - Combining the Best of MHT and GBT for High Value Target Tracking

Maintaining tracks on High-Value Targets (HVTs) in dense multi-target environments remains a computationally challenging problem. Approaches must trade off hypothesis management with state estimation accuracy in the presence of finite sensing and computational capabilities. This problem becomes more difficult when sensors provide additional, infrequent features: information that correlates track states over long timescales such as target size and color, or unique identifiers such as a license plate. Traditional real-time Multi-Hypothesis Tracking (MHT) algorithms must prune hypotheses before feature information arrives, often removing the correct association hypothesis from the solution space. Graph-Based Track stitching (GBT) algorithms suffer from two related problems: they rely on an upstream tracking algorithm to correctly associate measurements across short timescales, and must still associate tracks in the presence of infrequent feature information. As a result, the HVT tracking problem requires correctly assigning reports to tracks on both short and long timescales. In this paper, we extend the Approximate Track Automata (ATA) algorithm to perform dynamic hypothesis management given a set of HVT hypotheses and feature information models. The original ATA algorithm applied a single strategy to manage the entire hypothesis space; we tailor that approach here given HVTs and target features. We compare traditional tracking metrics such as root mean square error, probability of track, and track purity for HVT and background targets. In addition, we investigate the effect of scaling the number of Integer Linear Program (ILP) variables, i.e. the number of MHT and ATA hypotheses, on these metrics. Interestingly, we note that while solving ILPs is (in general) NP-complete, the ILP constraint matrices and cost vectors contain structure that often results in efficient runtimes in practice. We offer possible explanations as to why the ILP problem structure allows this near-polynomial runtime.
An Innovative Linear Complexity Computation for Cryptographic Sequences

A simple algorithm to compute the linear complexity of binary sequences with period a power of 2 has been proposed. The algorithm exploits the fractal structure of the binomial representation in this kind of binary sequences. The application of the general algorithm to a particular family of cryptographic sequences (generalized sequences) improves its performance as decreases the amount of sequence to be processed.
From Control to Coordination: Mahalanobis Distance-Pattern (MDP) Approach

It is pointed out in this paper that we have been discussing intelligence with focus on control, but coordination becomes increasingly important. In other words, intelligence up to now has been knowledge-based, but now we need to be wisdom-focused, because changes take place frequently and extensively and in an unpredictable manner. We need to interact directly with the outside world to adapt to such rapidly changing environments and situations. To describe it another way, we need to go ahead by trial and error. Thus, Pragmatic approach, PDSA, plays an important role. But to learn from failures, we need a quantitative performance indicator to improve. It is pointed out in this paper that non-Euclidean Distance, Mahalanobis Distance (MD), which does not require orthonormality and units enables simple expansion of dimensionality and easy and fast processing. So, it serves as an excellent quantitative performance indicator. Further, if we introduce pattern approach and integrate MD and pattern, then we can have a performance indicator, which is not only quantitative, but also holistic.
A Characterization of Belief Merging Operators in the Regular Horn Fragment of Signed Logic

In this paper, we present a set of logical postulates for belief merging in the set of all regular signed Horn formulas. Proving a representation result, we show that regular Horn merging in signed logic can be seen as an aggregation problem on rankings of possible interpretations in this many-valued setting.
Cyber Secure and Resilient Approaches for Feature Based Variation Management

A joint INCOSE/NDIA project is exploring the intersection between systems security engineering and product line engineering teams to develop cyber secure and resilient approaches for feature-based variation management. The project team is investigating existing approaches and developing new approaches to implement systems security in product line design, apply patterns for product line architectures that address systems security, and define variation management approaches for secure and resilient product line products and shared assets.
Short-range Robotic Navigation and Exploration Tasks via Deep Q-Networks for Biomedical Applications

This research is focused on the performance of a Deep Reinforcement Learning method on an agent (mobile robot) in a simulated virtual environment (Operating Room) for medical applications. The purpose of this research is to compare suitable decisive actions taken by the agent to achieve its goal target. Executing this goal requires the implementation of a reward-penalty system for observation and analysis. The agent's accumulated reward is based on the best-navigated decision to avoid collisions; solely generating an intelligent agent system. We reviewed previous works on the impact of Deep Reinforcement Learning algorithms on an agent in areas of navigation and exploration. Adopting a Deep Reinforcement Learning method and a physical simulator, we trained and tested the agent using existing environments and our modeled operating room, respectively. Measuring the positive reward output of the experiment with different parameters of the algorithm such as the learning rate, maximum Q-value and the average time to attain its goal position, we presented our work with plots of the experiment and compared it with a widely known traditional method. Our experimental results indicated that the agent achieved a high positive reward of 3800 in our operating room environment with a learning rate of 0.5. Our research aimed at training an agent to make intelligent decisions in achieving its goal destination without prior experience and input data. Reinforcement Learning provides a structure for robotics to function effectively; utilizing and engaging a robot to navigate and explore in any given environment.
Application of the armament cyber assessment framework

As the Army modernizes, its weapon systems are becoming increasingly more cyber dependent. This increased connectivity provides incredible opportunities, but also introduces new risks. This paper introduces the Armament Cyber Assessment Framework (ACAF), a schema for creating security assessment workflows integrated into the design process. The goal of ACAF is to introduce a security oriented mindset into the solution prior to release, and to provide meaningful results at every level. This goal is accomplished through the study and incorporation of multiple industry leading frameworks into a uniquely iterative process. ACAF is implemented for testing via the Global Vulnerability Assessment and Penetration Platform (GVAPP). GVAPP works to provide automated vulnerability information during the armament design process. It offers meaningful risk calculus to armament designers without cyber security backgrounds to mitigate potential vulnerabilities prior to fielding the system. This work focuses on military applications, but is applicable to similar civilian platform technologies.
Intelligent Multi-objective Anomaly Detection Method Based on Robust Sparse Flow

To meet the needs of transportation systems for smart scenic security services, real-time detection and identification of traffic anomalies with high accuracy is essential. Based on the multi-objective sparse optical flow estimation method based on KLT algorithm, an improved algorithm for robust sparse optical flow is designed. The Forward-Backward error calculation method was used to eliminate the error optical flow generated by the KLT algorithm and the robustness of optical flow was improved. The proposed algorithm was verified by the actual traffic scene monitoring example, and the anomaly detection accuracy is above 80%. Furthermore, it has good detection effect on the benchmark dataset.
Efficient Load-Time Diversity for an Embedded Real-Time Operating System

Cyber-physical systems (CPS) are threatened by cyber attacks just as any computing system. Even worse, due to them being embedded into the physical world, consequences can be catastrophic. The widespread use of unsafe languages and limited operating system protections makes code-reuse attacks particularly dangerous to smaller CPS. Existing effective countermeasures are either not applicable because resources are limited, or they introduce an unacceptable overhead. In this work, we propose a fine-grained load-time software diversity approach that is enabled by compile-time preparations. Its linear-time loading algorithm makes it feasible for resource-constrained CPS. We demonstrate our approach by fully diversifying an application including the real-time operating system FreeRTOS on an ARM real-time microcontroller. Our performance evaluation using the TACLe benchmark suite shows that the worst-case execution time overhead is acceptable.
Towards Building Data Trust and Transparency in Data-Driven Business Applications

In view of deriving business value from their (product) data, organisations need to adopt the right method and technology to analyse these data to infer new insights and business intelligence. This is feasible only with a certain guarantee on the completeness, trustworthiness, consistency and accuracy of the data. Thus, building trust in acquired (product) data and its analytics is pivotal if we are to realise its full benefits. To this end, we explore different technologies for building data trust, such as Blockchain, traditional distributed databases and trusted third party platforms, in combination with security algorithms. In this paper, we present a Blockchain-based solution for building data trust, based on which we designed a system prototype as a proof-of-concept.
On Signifier Things and Signing-as-Event

Research in mathematical cognition and learning has shown interest in semiotics—the science of signs—for a long time. Mathematical forms tend to be theorized as sign(ifier) things that refer to something else. The apotheosis of this approach exists in/as the Theory of Knowledge Objectification (Radford 2013), which holds that knowledge is externalized in mathematical forms that can be shared within groups of individuals. These mathematical forms thus refer to something existing on the inside of the person that is otherwise inaccessible. The forms are signifier–signs pointing to something else (e.g., individual, subjective “meaning”). Thus, a common theme in the literature on mathematical cognition and learning is how students come to be confronted with signifier–signs for which they have to construct meaning. Here, the signifier–sign is treated as if it could exist outside of its relations to the things signified. Long ago, such an approach to the question of the signifier–sign has been subject to severe critique from pragmatist philosophers, who suggest that
A High Energy-Efficiency Inference Accelerator Exploiting Sparse CNNs

The significantly growing computation and memory demands have become a bottleneck for the application of convolutional neural networks (CNNs). Model compression is an efficient method to accelerate CNNs. However, the commonly designed architectures are not suitable for compressed models and waste large computational resources on zero operands. In this work, we propose a flexible CNNs inference accelerator on FPGA utilizing uniform sparsity introduced by pattern pruning to achieve high performance. Our accelerator architecture exploits different input & output parallelism for sparse computation to maximize the utilization of computing arrays. A dynamically adjustable mechanism is designed to deal with the unbalanced workload. What's more, a novel data buffering structure with slightly rearranged sequences is applied to address the challenge of access conflict. The experiments show that our accelerator can achieve 316.4 GOP/s ~ 343.5 GOP/s for VGG-16 and ResNet-50.
Colour image encryption using Gaussian apertured real-preserving fractional Mellin transform in diffraction domain

A colour image encryption scheme based on Gaussian apertured real-preserving fractional Mellin transform (GARPFrMT) in diffraction domain and chaos permutation is proposed. Real-preserving fractional transform in diffraction domain using Gaussian aperture is constructed to ensure that the output is real, which is convenient for storage and communication. And the Gaussian aperture, as a soft aperture, can improve the intensity distributions of the output light, which contributes to prevent possible attacks for obtaining some useful information from the marginal leakage of light in the optical encryption systems. The colour space rotation transform is first used to convert the colour image from standard RGB space to R′G′B′ space. And then, the three new colour components are transformed by GARPFrMT with three different diffraction channels, respectively. A 3D-logistic map is utilized to further scramble the three-colour components. At last, a logistic-sine map is used to perform Xor operation with three components, respectively. Simulation results demonstrate that the proposed algorithm is feasible and robust to noise attack and data loss.
Study on the Role and Approach of Culture Background Knowledge Dissemination Based on Information Technology Application

Abstract In English teaching, it should be attached great importance to the infiltration of Culture Background Knowledge. Teachers should provide students with Culture Background Knowledge by means of information technology. Starting from the teaching practice, this paper explores the conjunction of information technology and culture background knowledge teaching, and discusses how to infiltrate English Culture Background Knowledge in information technology teaching from multiple perspectives.
Shaping Our Mental Model of Security

The IT industry’s need to distinguish new products with new looks, new experiences, and new user interface designs is bad for cybersecurity. It robs users of the chance to transfer previously acquired security-relevant knowledge to new products and leaves them with a poor mental model of security. Starting from a comparison with physical safety, we explore and sketch a method to help users develop a useful mental model of security in cybersystems. A beneficial side-effect of our methodology is that it makes precise what security requirements the user expects the system to fulfill. This can be used to formally verify the system’s compliance with the user’s expectation.
A Method for Ranking Tourist Attractions based on Geo-tagged Photographs and Image Quality Assessment

Recently, tourism has become a development emphasis for many countries because international tourism can bring huge revenues; it can also positively affect increased long-run economic growth. However, in this era of complex information, it is hard to get integrated tourist information on the Internet. Consequently, tourists might spend a lot of time to search and compare different information and then decided their travel itinerary. To deal with this issue, we propose a formula for ranking tourist attractions by analyzing geo-tagged photographs on Flickr in this paper. In this way, tourists can save their time to find their interest tourist attractions readily. Moreover, our proposed method includes different aspects such as image quality assessment (IQA), the sentiment of comment, and the popularity of tourist attraction which can evaluate the attractive level of tourist attraction. Especially, we provide different ranking results for local residents and foreign visitors.
Optimization of Search Space for Finding Very Short Lattice Vectors

Shortest vector problem on lattices (SVP) is a well-known algorithmic combinatorial problem. The hardness of SVP is a foundation for the security of Lattice-based cryptography, which is a promising candidate of the post-quantum cryptographic algorithms. Therefore, many works have focused on the estimation of the hardness of SVP and the construction of efficient algorithms. Recently, a probabilistic approach has been proposed for estimating the hardness, which is based on the randomness assumption. The approach can estimate quite accurately the distribution of very short lattice vectors in a search space. In this paper, a new method is proposed for optimizing a box-type search space in random sampling by this probabilistic approach. It has been known empirically that the tail part of the search space should be more intensively explored for finding very short lattice vectors efficiently. However, it was difficult to adjust the search space quantitatively. On the other hand, our proposed method can find the best search space approximately. Experimental results show that our method is useful when the lattice basis is small or already reduced in advance.
A note on secure multiparty computation via higher residue symbols

Abstract We generalize a protocol by Yu for comparing two integers with relatively small difference in a secure multiparty computation setting. Yu's protocol is based on the Legendre symbol. A prime number p is found for which the Legendre symbol (· | p ) agrees with the sign function for integers in a certain range {− N , . . . , N } ⊂ ℤ. This can then be computed efficiently. We generalize this idea to higher residue symbols in cyclotomic rings ℤ[ ζ r ] for r a small odd prime. We present a way to determine a prime number p such that the r -th residue symbol (· | p ) r agrees with a desired function <m:math xmlns:m="http://www.w3.org/1998/Math/MathML" display="inline"> <m:mrow> <m:mi>f</m:mi> <m:mo>:</m:mo> <m:mi>A</m:mi> <m:mo>→</m:mo> <m:mrow> <m:mo>{</m:mo> <m:mrow> <m:msubsup> <m:mi>ζ</m:mi> <m:mi>r</m:mi> <m:mn>0</m:mn> </m:msubsup> <m:mo>,</m:mo> <m:mo>…</m:mo> <m:mo>,</m:mo> <m:msubsup> <m:mi>ζ</m:mi> <m:mi>r</m:mi> <m:mrow> <m:mi>r</m:mi> <m:mo>−</m:mo> <m:mn>1</m:mn> </m:mrow> </m:msubsup> </m:mrow> <m:mo>}</m:mo> </m:mrow> </m:mrow> </m:math> f:A \to \left\{ {\zeta _r^0, \ldots ,\zeta _r^{r - 1}} \right\} on a given small subset A ⊂ ℤ[ ζ r ], when this is possible. We also explain how to efficiently compute the r -th residue symbol in a secret shared setting.