Reviewerly/exHarmony huggingface dataset:
Bio-inspired Structure Identification in Language Embeddings

Word embeddings are a popular way to improve downstream performances in contemporary language modeling. However, the underlying geometric structure of the embedding space is not well understood. We present a series of explorations using bio-inspired methodology to traverse and visualize word embeddings, demonstrating evidence of discernible structure. Moreover, our model also produces word similarity rankings that are plausible yet very different from common similarity metrics, mainly cosine similarity and Euclidean distance. We show that our bio-inspired model can be used to investigate how different word embedding techniques result in different semantic outputs, which can emphasize or obscure particular interpretations in textual data.
On-Chip Randomization for Memory Protection Against Hardware Supply Chain Attacks to DRAM

Dynamic Random Access Memory (DRAM) is widely used for data storage and, when a computer system is in operation, the DRAM can contain sensitive information such as passwords and cryptographic keys. Therefore, the DRAM is a prime target for hardware-based cryptanalytic attacks. These attacks can be performed in the supply chain to capture default key mechanisms enabling a later cyber attack or predisposition the system to remote effects. Two prominent attack classes against memory are the Cold Boot attack which recovers the data from the DRAM even after a supposed power-down and Rowhammer attack which violates memory integrity by influencing the stored bits to flip. In this paper, we propose an on-chip technique that obfuscates the memory addresses and data and provides a fast detect-response to defend against these hardware-based security attacks on DRAM. We advance the prior hardware security research by making two contributions. First, the key material is detected and erased before the Cold Boot attacker can extract the memory data. Second, our solution is on-chip and does not require nor depend on additional hardware or software which are open to additional supply chain attack vectors. We analyze the efficacy of our scheme through circuit simulation and compare the results to the previous mitigation approaches based on DRAM write operations. Our simulation and analysis results show that purging key information used for address and data randomization can be achieved much faster and with lower power than with typical DRAM write techniques used for sanitizing memory content. We demonstrate through circuit simulation of the key register design a technique that clears key information within 2.4ns which is faster by more than two orders magnitude compared to typical DRAM write operations for 180nm technology, and with a power consumption of 0.15 picoWatts.
Few-Shot Learning for Vehicle Make &amp; Model Recognition: Weight Imprinting vs. Nearest Class Mean Classifiers

In this paper, we study few-shot learning problem with an application to vehicle make and model recognition task in traffic surveillance images. While they may be considered as a robust alternative to ALPR based vehicle model recognition, image based vehicle recognition systems typically fails when an unseen vehicle appears in the scene. Few-shot learning methods may offer a promising solution in resolving this unseen class problem for vehicle model recognition task. In this study, we compare two popular few-shot learning approaches on vehicle model recognition task, namely weight imprinting and nearest class mean classifier. Objective of the proposed approach is to yield good classification performance on the novel classes while keeping the high accuracy rate of the base (existing) classes. We evaluate the effectiveness of the proposed approach using 8423 test images belonging to 100 novel and 100 existing categories. Experimental results have shown that the nearest class mean classifier outperforms weight imprinting on this task with an overall accuracy rate of 82% compared to 65% of weight imprinting.
Stimulating the development of soft skills in Software Engineering Education through Design Thinking

The development of soft skills is essential for future software engineers since the development process requires creativity, communication and problem-solving skills. Instructors can adapt pedagogical strategies to train students' soft skills and the technical knowledge needed for the profession. Design Thinking (DT) is a methodology that promotes the production of creative and innovative software solutions. Teaching DT in Software Engineering (SE) courses can promote students with an interdisciplinary teaching practice that stimulates soft skills to understand and explore problems and design innovative software solutions. This paper presents research exploring how DT provides practical experiences and it supports the development of students' soft skills from SE courses. We proposed and developed a dynamic based on the DT phase that simulates practical situations of a software project. The students' perceptions of the dynamics were analyzed qualitatively. The results showed that the dynamics promoted practical experiences and the development of soft skills, such as problem-solving, creativity, teamwork, diversity, and critical thinking.
Fast computation of yearly averages of useful quantities for solar engineering

In solar engineering, many simulations require the computation of averages over a year of quantities such as the efficiency of solar plants. In the case of stationary quantities, i.e., that do not depend on the past history but only on the present conditions, time averages can be replaced by averages over the solar position, which are much faster to compute. Through a suitable choice of coordinates for the Sun position, namely, the hour angle and a declination-equivalent coordinate, the problem can be rewritten as a comparatively simple expression involving the sum of two double integrals; the solution can then be obtained numerically via suitable fast quadrature methods. The average over a year can be computed by transforming the dependence on irradiation, cloudiness, temperature or other instantaneous parameters into two dependences on Sun position – one for winter-spring (ascending declination) and one for summer-winter (descending declination). The proposed method is substantially faster than usual time integration over a year: the speedup factor ranges from 18 to almost 700 in the considered examples. It is especially well-suited to computations that require many yearly averages and could even be unfeasible otherwise. Examples of such applications include multi-parameter optimisations of the configuration of a power plant with the goal of maximizing the overall year production.
Investigating Deep Q-Network Agent Sensibility to Texture Changes on FPS Games

Graphical updates are very common in modern digital games. For instance, PC game versions usually receive higher resolution textures after some time. This could be a problem for autonomous agents trained to play a game using Convolutional Neural Networks. These agents use the pixels of the screen as inputs and changing them could harm their performance. In this work, we evaluate agents' sensibility to texture changes. The agents are trained to play a First-Person Shooter game and then are presented to different versions of the same scenario, in which the only difference among them is texture changes. As the testbed, we use a ViZDoom scenario with a static monster that should be killed by the agent. Four agents are trained using Deep Q-Networks in four different scenarios. Then, every agent is tested in all four scenarios. We show that although every agent can learn the behaviors to win the game when playing the same version in which it was trained, they cannot generalize to all other versions. Only in one case, the agent had a good performance in a different scenario. Most of the time, the agent moved randomly or just stood still, and shot continuously, indicating that it could not understand the current screen. Even when the background textures were kept the same, the agent could not identify the enemy. Thus, to ensure proper behavior, an agent needs to be retrained not only if the problem changes, but also when only the visual aspects of the problem are modified.
House Price Prediction with An Improved Stack Approach

Abstract As a typical framework in ensemble learning, Stacking creates a multi-layer learner by integrating the learning results of different individual learners. Despite a general improvement in accuracy, multiple layers in stacking results in high computation cost. In addition, some individual learners do not perform better than others in single-model prediction task. To solve the above drawbacks of traditional stacking, this paper proposes an improved Stacking algorithm based on the concept of “weighted attribute”. For each individual learner, a corresponding weight will be calculated based on its prediction performance on training data. Weights will then be used as evaluation criteria for model performance. Models with larger weights will be selected as attributes for meta-learner. When improved Stacking algorithm is applied on the same dataset, experiment results indicate a slight improvement in prediction accuracy and a relatively large reduction in computation cost. This demonstrates practical application value of the improvement.
RITA: Efficient Memory Allocation Scheme for Containerized Parallel Systems to Improve Data Processing Latency

The emerging resource-sharing container-based virtualization is prevalent in IT, as it is a much lighter deployment in the cloud environment compared to VM-based virtualization. Distributed data-processing workloads executing in parallel take advantages of resource sharing, fast delivery, and excellent portability of containerization, but also suffer from resource competition and performance interference. Especially for memory virtualization, data-processing frameworks allocate physical memory (i.e., RAM) and swap to applications specified by users, without considering cache-characteristics and parallelism of applications, which induces performance degradation and significantly protracted latency which is worse given over-provisioning. We design an efficient memory allocation scheme (RITA) for containerized parallel systems to improve data processing latency. RITA monitors memory usage and cache characteristics of applications, and dynamically re-allocates memory resources. We implement RITA in a real-world system, which can easily migrate to other container-based virtualization environments. Our experimental results show that RITA provides remarkable latency improvement for memory intensive distributed data-processing workloads.
Efficient FPGA design for Convolutions in CNN based on FFT-pruning

Fast algorithms of convolution, such as Winograd and fast Fourier transformation (FFT), have been widely used in many FPGA-based CNN accelerators to reducing the complexity of multiplication. The core idea for those fast algorithms is reducing the number of multiplication at the cost of more additions. However, increased additions take up a significant portion in the whole LUT resources in many cases, which forms a new bottleneck in the corresponding hardware design. In this paper, we theoretically analyze the relationship between the reduced multiplications and the increased additions, and propose an reduced complexity fast FFT convolution algorithm by intelligently employing the FFT-pruning method to remove redundant additions. Compared with the state-of-the-art algorithm, our algorithm can reduce more than 50% of additions. Moreover, the proposed algorithm has better numerical accuracy and comparable multiplication complexity compared to the most efficient Winograd algorithm. Additionally, an efficient reconfigurable architecture of the proposed algorithm is also developed to accelerate convolutional layers with various kernel sizes. Implemented with Xilinx ZC706, the proposed architecture achieves 200.6 GOPS on convolutional layers of ResNet-50 with 61% higher resources efficiency with respect to LUT consumption compared to prior arts.
Manhattan Distance based Nearest Vicinity Guided Filter Design for High Density Salt and Pepper Noise Removal

Elimination of highly densified salt and pepper noise from digital images is an exigent business in image processing domain. Numerous state-of-art filters have shown judicious outcomes at low and average noise concentrations. In this paper a nearest vicinity guided two-step impulse noise filter (NVGINF) is proposed. The detection phase segregates the un-corrupted pixels by neglecting the ‘0’ (pepper noise) and ‘255’ (salt noise) pixel intensities from the images. In the removal phase, the corrupted pixels are restored by substituting them with detected un-corrupted pixels located at least Manhattan distance, using equivalent mean-median measures. NVGINF is assessed upon three test images using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM) and Average Run Time (ART). It has been witnessed that the proposed NVGINF demonstrates viable outcome up against a number of contemporary filters specifically at high noise densities.
Independent Approximates enable closed-form estimation of heavy-tailed distributions

A new statistical estimation method, Independent Approximates (IAs), is defined and proven to enable closed-form estimation of the parameters of heavy-tailed distributions. Given independent, identically distributed samples from a one-dimensional distribution, IAs are formed by partitioning samples into pairs, triplets, or nth -order groupings and retaining the median of those groupings that are approximately equal. The pdf of the IAs is proven to be the normalized nth power of the original density. From this property, heavy-tailed distributions are proven to have well-defined means for their IA pairs, finite second moments for their IA triplets, and a finite, well-defined (n-1)th moment for the nth grouping. Estimation of the location, scale, and shape (inverse of degree of freedom) of the generalized Pareto and Student’s t distributions are possible via a system of three equations. Performance analysis of the IA estimation methodology for the Student’s t distribution demonstrates that the method converges to the maximum likelihood estimate. Closed-form estimates of the location and scale are determined from the mean of the IA pairs and the second moment of the IA triplets, respectively. For the Student’s t distribution, the geometric mean of the original samples provides a third equation to determine the shape, though its nonlinear solution requires an iterative solver. With 10,000 samples the relative bias of the parameter estimates is less than 0.01 and the relative precision is less than ± 0.1. Statistical physics applications are carried out for both a small sample (331) astrophysics dataset and a large sample (2 x 10 8 ) standard map simulation. • Report of new discovery regarding the estimation of heavy-tailed distributions. • The n-tuple Independent Approximate samples are distributed as the normalized nth-power of the original distribution. • Verification that the closed-form estimate approaches the maximum likelihood estimation. • Computational experiments include examples from astrophysics and nonlinear simulations.
"Is anybody there?"

Conversational Systems (CSs) are increasingly being used and receiving investments. However, these systems can present problems that discourage users from using them. This paper aims to verify the opinions of Brazilian users and ex-users of CSs about the use of these systems and their possible difficulties. We conducted an online survey with several questions about the use of CSs and obtained 113 respondents from different knowledge areas, such as law, agronomy, health, computing, among others. Results reveal some issues in the Brazilian context that negatively influence the use of CSs, such as accent, lack of support for the Portuguese language, and internet access problems. Despite this, results show that respondents consider CSs easy to use and satisfactory. We hope our results can contribute to improving these systems for the Brazilian context, providing insights for the exploration of new strategies for designers and developers.
HOOVER: Leveraging OpenSHMEM for High Performance, Flexible Streaming Graph Applications

As the adoption of streaming graph applications increases in the finance, defense, social, and other industries so does the size, velocity, irregularity, and complexity of these streaming graphs. Most existing frameworks for processing streaming, dynamic graphs are too limited in scalability or functionality to efficiently handle these increasingly massive graphs. Many frameworks are either built for shared memory platforms only (limiting the size of the graph that can be stored) or for distributed platforms, but run on slow, high overhead, interpreted, and bulk synchronous platforms. This paper introduces HOOVER, a high performance streaming graph modeling and analysis framework built from scratch to scale on high performance systems and extremely dynamic graphs. HOOVER offers similar APIs to previous streaming graph frameworks, but sits on top of a high performance runtime system designed for modern supercomputers. HOOVER leverages an eventually-consistent consistency model to improve scalability, and offers a number of unique features to users. On micro-benchmarks, HOOVER is shown to be comparable or faster than existing high performance and distributed graph frameworks. Using mini-apps, we also show that HOOVER easily scales to 2,048 PEs on more realistic applications.
Research on Speech Synthesis Technology Based on Rhythm Embedding

Abstract In recent years, Text-To-Speech (TTS) technology has developed rapidly. People have also been paying more attention to how to narrow the gap between synthetic speech and real speech, hoping that synthesized speech can be integrated with real rhythm. A rhythmic feature embedding method for Text-To-Speech was proposed in this thesis based on Tacotron2 model, which has arisen in the field of TTS in recent years. Firstly, rhythmic feature extraction through World vocoder can reduce redundant information in rhythmic features. Then, rhythmic feature fusion based on Variational Auto-Encoder (VAE) network can enhance rhythmic information. Experiments are carried out on the data set LJSpeech-1.0, and then subjective evaluation and objective evaluation are carried out on the synthesized speech respectively. Compared with the comparative literature, the subjective blind hearing test (ABX) score increased by 25%. At that same time, the objective Mel Cepstral Distortion value (MCD) declined to 12.77.
Attributed Graph Clustering via Deep Adaptive Graph Maximization

The following topics are dealt with: learning (artificial intelligence); pattern classification; feature extraction; pattern clustering; convolutional neural nets; graph theory; optimisation; data mining; social networking (online); Internet of Things.
A New Automatic Chinese Poetry Generation Model Based On Neural Network

Chinese ancient poetry has been a favorite literary genre for thousands of years. Nowadays, it is still read and ancient Chinese poets are honored. Recently, many websites provide automatic Chinese poetry generation service based on neural network. My research goal is to improve the quality of the poetry generation, i.e., making it as close as possible to the real poetry written by poets. To achieve this goal, I propose a new context-aware Chinese poetry generation method based on sequence-to-sequence framework. I generate a new concept called keyword team which is a combination of all the keywords to capture the context of the Chinese poetry. Then I use the keyword, the keyword team and the previously generated lines to generate the current line in a poem. I have already implemented the new context-aware Chinese poetry generation model called KPG (Keyword team based Poetry Generation). I have compared this method with state-of-the-art ones using loss function and human judgement. The comprehensive evaluation result show that our proposed method outperforms the state-of-the-art ones.
Graphon Filters: Signal Processing in Very Large Graphs

Graph filters are at the core of network information processing architectures, with applications in machine learning and distributed collaborative systems. Yet, designing filters for very large graphs is challenging because filter design techniques do not always scale with graph size. We overcome this by using graphons, which are infinite-dimensional representations of graphs that are at once random graph models and limit objects of sequences of graphs. Explicitly, we define graphon filters and leverage convergence properties of graph sequences and spectral properties of both graphs and graphons to show that graph filters converge to graphon filters. Filters designed on a graphon can therefore be applied to finite graphs sampled from it with guaranteed convergence properties. We illustrate our findings in two experiments, which corroborate filter response convergence and illustrate transferability of graph filters even in graphs that are not related to one another through a graphon, but that are built from the same type of data.
Enhanced Attention-based Back Projection Network for Image Super-Resolution in Sensor Network

In this article, we propose a novel method for image restoration in sensor network. Deep learning based image Super-Resolution (SR) is such process which takes low-resolution image as input and outputs high-resolution one by an end-to-end trainable architecture. It has shown rapid development in this field by constantly deepening and widening the SR model or combining with other specially designed structures. Recently non-local attention mechanism is proposed and introduced into SR field which focuses on the self-similarity of single feature or difference between channels, but those methods don’t use the mechanism to explore the relationship between features in different layers. In this article, we design a Back Projection Attention Module to learn the cross-relation between different types of image degradation and high-resolution components. Then we construct our network by stacking the iterative up-sampling and down-sampling process and concatenating all outputs of up-sampling process to finally improve the ability of characteristic expression. Extend experiments on four commonly used datasets show the proposed model can improve the performance in quantitative and qualitative measurements.
Utility Promises of Self-Organising Maps in Privacy Preserving Data Mining

Data mining techniques are highly efficient in sifting through big data to extract hidden knowledge and assist evidence-based decisions. However, it poses severe threats to individuals’ privacy because it can be exploited to allow inferences to be made on sensitive data. Researchers have proposed several privacy-preserving data mining techniques to address this challenge. One unique method is by extending anonymisation privacy models in data mining processes to enhance privacy and utility. Several published works in this area have utilised clustering techniques to enforce anonymisation models on private data, which work by grouping the data into clusters using a quality measure and then generalise the data in each group separately to achieve an anonymisation threshold. Although they are highly efficient and practical, however guaranteeing adequate balance between data utility and privacy protection remains a challenge. In addition to this, existing approaches do not work well with high-dimensional data, since it is difficult to develop good groupings without incurring excessive information loss. Our work aims to overcome these challenges by proposing a hybrid approach, combining self organising maps with conventional privacy based clustering algorithms. The main contribution of this paper is to show that, dimensionality reduction techniques can improve the anonymisation process by incurring less information loss, thus producing a more desirable balance between privacy and utility properties.