Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-aspect-sentiment Recommendation Models Based on Novel Similarity Measure Methods

The explosive growth of e-commerce has led to the development of the recommendation system. The recommendation system aims to provide a set of items that meet users’ personalized needs through analyzing users’ consumption records. However, the timeliness of purchasing data and the implicity of feedback data pose severe challenges for the existing recommendation methods. To alleviate these challenges, we exploit the user’s consumption records from the perspectives of user and item, by modeling the data on both item and user level, where the item-level value reflects the grade of item, and the user-level value reflects the user’s purchase intention. In this article, we collect the description information and the reviews of the items from public websites, then adopt sentiment analysis techniques to model the similarities on user level and item level, respectively. In particular, we extend the traditional latent factor model and propose two novel methods—Item Level Similarity Matrix Factorization (ILMF) and User Level Similarity Matrix Factorization (ULMF)—by introducing two novel similarity measure methods. In ILMF and ULMF, the consistency between latent factors and explicit aspects is naturally incorporated into learning latent factors of the users and items, such that we can predict the users’ preferences on different items more accurately. Moreover, we propose Item-User Level Similarity Matrix Factorization (IULMF), which combines these two methods to study their contributions on the final performance. Experimental evaluations on the real datasets show that our methods outperform the baseline approaches in terms of both the precision and NDCG.
Towards introducing just-in-time compilation in a Modelica compiler

We are investigating ways of introducing just-in-time compilation in a standard-compliant Modelica compiler, the Open-Modelica compiler (OMC). The main motivations are enabling extensions to support dynamically varying model structure, faster compilation, and faster recompilation of models after changes. We are investigating two approaches. The first approach is to adapt the low-level OpenModelica intermediate representation (IR) before code generation to be compatible with LLVM. In that way we can avoid generating intermediate C-code and instead generate LLVM IR in memory for just-in-time compilation (JIT). The second approach is to translate OMC itself written in MetaModelica to Julia, and thereby gain access to the JIT capabilities of LLVM. Another benefit of the second approach is the access to the Julia ecosystem, including a rich set of libraries for numerical computing. We have done a preliminary investigation of both approaches, with measurements on a selected sample of algorithms, and discovered that compilation-time of generated Julia code is slower compared to generating LLVM IR directly. We conclude that providing a standard-compliant Modelica compiler which supports a dynamically varying model structure is feasible and possible, and we believe that such a compiler can be provided by using Julia or MetaModelica.
Towards a Generalized System for Generative Engineering

Product design based on mechanical performance requirements and with given boundary conditions is a frequent and demanding engineering problem. In most cases, effectiveness of the design is limited to expertise of the respective designer, which also results in inefficient and incomplete exploration of the product's design space. With a large amount of data available and recent advancements in hardware technology, data-driven methods have become an increasingly common strategy for problems that are difficult to formulate or expensive to solve by creating a physical model, but such methods often lack the application perspective; they are not yet widely applied especially in engineering product development. Generative design (here, more precisely generative engineering) is one such technique which has recently gained importance. Although many preliminary studies demonstrate plausible results from data-driven generative design approaches, a closeloop-3D-generative-design-pipeline that achieves full automation and optimization of the design with respect to the product's functional requirements is still in its nascent stage. This paper proposes a generalized hybrid framework comprised of both data-based and physics-based methods to achieve this objective, as represented by the teaser figure 1. An overview of various possibilities for individual modules of the framework is also discussed.
Turbo-GTS

In mobile crowdsourcing, workers are financially motivated to perform as many self-selected tasks as possible to maximize their revenue. Unfortunately, the existing task scheduling approaches in mobile crowdsourcing fail to consider task execution duration and do not scale for massive tasks and large geographic areas. In this article, we propose a novel framework, Turbo-GTS, in support of large-scale geo-task scheduling, with the objective of identifying an optimal task assignment for each worker to maximize the total number of tasks that can be completed for an entire worker group, given the geographic locations of each task and each worker. Since the exact solution to the geo-task scheduling problem is computationally intractable, we first propose two sub-optimal approaches (least cost neighbor with particle filtering and non-urgency degree particle filtering with iterative clustering) based on particle filtering and DBSCAN for the single-worker geo-task scheduling problem. We then extend our work to solve the multi-worker geo-task scheduling problem by proposing two space partitioning-based methods (QT-NNH and QT-NUD), which leverage point-region quadtree to ensure workload balancing. The effectiveness and efficiency of the four proposed approximate solutions are verified by our extensive experiments using both real and synthetic data. Compared to state-of-the-art approaches, our proposed solutions are able to return a higher number of completed tasks for the worker group while reducing the computation cost by up to three orders of magnitude when coping with massive tasks distributed in large geographic areas.
Recurrent Neural Network Based Binary Code Vulnerability Detection

Vulnerability detection, especially vulnerability automatic detection, have long been an area of active research. Considering that RNN can model dependencies automatically, we propose a recurrent neural network (RNN) based method for binary code vulnerability detection. We collected correspond to 4 types of vulnerabilities, including CWE-134, CWE-191, CWE-401 and CWE-590.To find which RNN is better in vulnerability detection, we evaluated 6 different RNNs, including SRNN, BSRNN, LSTM, BLSTM, GRU and BGRU. The results demonstrated that BSRNN is better than others in vulnerability detection.
Deep Learning Based-Recommendation System: An Overview on Models, Datasets, Evaluation Metrics, and Future Trends

The growth of data in recent years has motivated the emergence of deep learning in many Computer Sciences related fields including Recommender System (RS). Deep learning has emerged as the solution; overcoming the obstacles of traditional recommendation models. Deep learning is able to enhance recommendation quality by learning non-linear and non-trivial user-item relationship, and extracting deep and abstract feature representations for users and items. However, deep learning in RS is still new and flourishing. The contribution of this paper is two-folds. Firstly, we will be providing several insights on the advances of RS focusing on deep-learning models, datasets and evaluation metrics. Secondly, we expand on the current trend and provide several possible research directions in the field of deep learning-based RS.
STEAR: Robust Step Counting from Earables

This paper shows that inertial measurement units (IMUs) inside earphones offer a clear advantage in counting the number of steps a user has walked. While step-count has been extensively studied in the mobile computing community, there is wide consensus that false positives are common. The main reason for false positives is due to limb and device motions producing the same periodic bounce as the human walk. However, when IMUs are at the ear, we find that many of the lower-body motions are naturally "filtered out", i.e., these noisy motions do not propagate all the way up to the ear. Hence, the earphone IMU detects a bounce produced only from walking. While head movements can still pollute this bouncing signal, we develop methods to alleviate the problem. Results show 95% step-count accuracy even in the most difficult test case -- very slow walk -- where smartphone and fitbit-like systems falter. Importantly, our system STEAR is robust to changes in walking patterns and scales well across different users. Additionally, we demonstrate how STEAR also bring opportunities for effective jump analysis, often important for exercises and injury-related rehabilitation.
Target Recognition Method based on Feature Fusion

In recent years, with the rapid development of machine learning and artificial intelligence, the problem of target recognition and classification has made a breakthrough. Single mode data cannot summarize the feature information of the target well, multi-view and multi-modal representation learning become a fast-growing direction in the field of machine learning and data mining. In the case that the single modal data sample is not enough, the multi-modal analysis method can not only make up for the difficult training problem caused by insufficient data, but also enhance the representation ability by extracting the complementary relationship between different view of data. Multi-model can improve the accuracy of target recognition. Therefore, this paper proposes an image feature extraction based on convolutional neural network (CNN) and a feature fusion method in the feature layer to better solve the classification problem. This method mainly consists of three steps. First, a convolutional neural network is trained and used to extract features of the images. Secondly, selecting an appropriate dimension in the extracted image features and fusing it with the basic attribute features of the target. Finally, the newly formed fusion features are used to train the classifier and perform comparative verification on the data set. Through experiments, it is finally found that multi-feature fusion can better represent the target, achieving better results on the target classification and recognition problem.
A deep convolutionary network for automatic detection of audio events

In the last years, a growing interest of the scientific community has been devoted to the design of efficient and effective solutions for automatically identifying sounds of interest in the field of audio surveillance. In this paper, we propose an event detection architecture based on a convolutional neural network. The network takes in input directly the raw sampled waveform, in order to reduce the information loss introduced by any type of preprocessing and is able to automatically learn the most important frequencies for the sounds of interests to be recognized. We evaluate the performance of the proposed approach over the MIVIA Audio Events dataset, widely adopted for benchmarking purposes, achieving the best results and, therefore, confirming its effectiveness with respect to state of the art methodologies, based on both traditional machine learning and deep learning methodologies.
An Augmented Translation Technique for Low Resource Language Pair: Sanskrit to Hindi Translation

Neural Machine Translation (NMT) is an ongoing technique for Machine Translation (MT) using an enormous artificial neural network. It has exhibited promising outcomes and has shown incredible potential in solving challenging machine translation exercises. One such exercise is the best approach to furnish great MT to language sets with a little preparing information. In this work, we inspect Zero-Shot Translation (ZST) for a low resource language pair. By working on high resource language pairs for which benchmarks are available, namely Spanish to Portuguese, and training on data sets(Spanish-English and English-Portuguese), we prepare a state of proof for ZST system that gives appropriate results on the available data. Subsequently, we test the same architecture for Sanskrit to Hindi translation for which data is sparse, by training the model on English-Hindi and Sanskrit-English language pairs. To prepare and decipher with the ZST system, we broaden the preparation and interpretation pipelines of the NMT seq2seq model in TensorFlow, incorporating ZST features. Dimensionality reduction of word embedding is performed to reduce the memory usage for data storage and to achieve faster training and translation cycles. In this work, existing helpful technology has been utilized imaginatively to execute our Natural Language Processing (NLP) issue of Sanskrit to Hindi translation. We have constructed a Sanskrit-Hindi parallel corpus of 300 sentences for testing. The data required for the construction of a parallel corpus has been taken from the telecasted news, published on the Department of Public Information, the state government of Madhya Pradesh, India website.
AR2Net

Business location selection is crucial to the success of businesses. Traditional approaches like manual survey investigate multiple factors, such as foot traffic, neighborhood structure, and available workforce, which are typically hard to measure. In this article, we propose to explore both satellite data (e.g., satellite images and nighttime light data) and urban data for business location selection tasks of various businesses. We extract discriminative features from the two kinds of data and perform empirical analysis to evaluate the correlation between extracted features and the business popularity of locations. A novel neural network approach named R2Net is proposed to learn deep interactions among features and predict the business popularity of locations. The proposed approach is trained with a regression-and-ranking combined loss function to preserve accurate popularity estimation and the ranking order of locations simultaneously. To support the location selection for multiple businesses, we propose an approach named AR2Net with three attention modules, which enable the approach to focus on different latent features according to business types. Comprehensive experiments on a real-world dataset demonstrate that the satellite features are effective and our models outperform the state-of-the-art methods in terms of four metrics.
A Multi-task Learning Approach Based on Convolutional Neural Network for Acoustic Scene Classification

Acoustic Scene Classification (ASC) aim to recognize an acoustic scene in audio signal records. The acoustic scene is a mixture of background sounds and various sound events, and sound events often determine the type of acoustic scene. However, in many research methods for acoustic scene classification, only a few people have noticed the important information of sound events. In this paper, we combine the ASC task and Sound Event Detection (SED) task, and propose a new CNN approach with multi-task Learning (MTL), which uses SED as an auxiliary task to pay more attention to the information of the sound event in the model. Besides, in view of the characteristic of the sound event with high-energy time-frequency components, we use Global Max Pooling (GMP) instead of the Fully Connected layer (FC) in the traditional CNN. The advantage is that the model focused on distinct high-energy time-frequency components of audio signals (sound event). Finally, extensive experiments are carried out on the TUT acoustic scene 2017 dataset. Our proposed CNN approach with MTL shows better generalization, and improves the Unweighted Average Recall (UAR) of 5.2% over the DCASE 2017 ASC baseline system.
Computational Sophistication of Games Programmed by Children

This article builds on prior work that aims to measure computational learning (CL) during middle school. Since game computational sophistication (GCS) has been used as a proxy for a student’s engagement in CL we build on their model to more completely describe the relationship between different types of building blocks of computer games and GCS. In doing so, we present a single quantitative measurement for GCS. Our model, called GCS 2.0, has face validity for 39 games, each programmed by a pair of middle school children. We choose four of these games, two with high GCS and two with low GCS, and discuss the computational building blocks found in each game. We do this to help the reader better understand our measurement of GCS and its relationship to CL.
Remodeling Numerical Representation for Text Generation on Small Corpus: A Syntactical Analysis

Data-to-text generation aims to generate natural language descriptions from non-linguistic data. Recent research on data-to-text generation often uses a neural encoder-decoder architecture due to its simplicity to work across multiple domain. In this study, we aim to investigate two input encoding strategies: (1) numeral encoding as baseline, and (2) numeral as sequence-of-character tokens as proposed solution in financial data-to-text systems. An empirical study on the financial dataset validates our initial hypothesis that the character-based representation performs comparable results in content selection and diversity towards the generated text descriptions.
HopliteBuf

HopliteBuf is a deflection-free, low-cost, and high-speed FPGA overlay Network-on-chip (NoC) with stall-free buffers. It is an FPGA-friendly 2D unidirectional torus topology built on top of HopliteRT overlay NoC. The stall-free buffers in HopliteBuf are supported by static analysis tools based on network calculus that help determine worst-case FIFO occupancy bounds for a prescribed workload. We implement these FIFOs using cheap LUT SRAMs (Xilinx SRL32s and Intel MLABs) to reduce cost. HopliteBuf is a hybrid microarchitecture that combines the performance benefits of conventional buffered NoCs by using stall-free buffers with the cost advantages of deflection-routed NoCs by retaining the lightweight unidirectional torus topology structure. We present two design variants of the HopliteBuf NoC: (1) single corner-turn FIFO (W → S) and (2) dual corner-turn FIFO (W → S+N). The single corner-turn (W → S) design is simpler and only introduces a buffering requirement for packets changing dimension from the X ring to the downhill Y ring (or West to South). The dual corner-turn variant requires two FIFOs for turning packets going downhill (W → S) as well as uphill (W → N). The dual corner-turn design overcomes the mathematical analysis challenges associated with single corner-turn designs for communication workloads with cyclic dependencies between flow traversal paths at the expense of a small increase in resource cost. Our static analysis delivers bounds that are not only better (in latency) than HopliteRT but also tighter by 2−3×. Across 100 randomly generated flowsets mapped to a 5×5 system size, HopliteBuf is able to route a larger fraction of these flowsets with <128-deep FIFOs, boost worst-case routing latency by ≈ 2× for mutually feasible flowsets, and support a 10% higher injection rate than HopliteRT. At 20% injection rates, HopliteRT is only able to route 1--2% of the flowsets, while HopliteBuf can deliver 40--50% sustainability. When compared to the W → Sbkp backpressure-based router, we observe that our HopliteBuf solution offers 25--30% better feasibility at 30--40% lower LUT cost.
The Invisible Structures of the Artworld

The Invisible Structures of the Artworld draws inspiration from the contemporary artworld processes through which artists and artworks are appreciated and gain recognition as well as the fact that the artworld structure at any given time is not entirely known. Another point from which this work draws inspiration is the approaches taken for social network analysis, the process of investigating social structures through the use of networks. The Invisible Structures of the Artworld is a data-driven work. It collects real-time social media data to identify artworld actors and finally visualize an artworld network whose structure becomes visible. Thus, it visualizes the global transformations of the contemporary artworld as well as the role and impact of artworld actors involved in the processes of the production and mediation of art.
The effect of inverted cone inserts design on granular behavior in silo

This paper proposes to study the effect of the insert design on behavior of granular flow during silo discharge. The mathematical model based on the principle of linear momentum and angular momentum for the motion of granular during silo discharge is presented. Discrete Element Method is applied to obtain the solution of the model. The silo with an inverted cone insert is used in this study. The impact of three different vertex angles of the insert including 30◦, 60◦ and 90◦ and four locations of the insert on flow pattern, velocity and wall pressure of granular flow during discharging process are investigated. The results show that the mathematical model can capture the granular behavior in the silo. The vertex angles and positions of the insert have significant effects on the flow pattern, the velocity field and wall pressure in the silo.
Malicious Code Detection Technology based on Bi-GRU and Self-attention

In view of the problems of the existing malicious code detection methods in heavy dependence on manual feature extraction and failure to extract the deep features of malicious codes, a malicious code detection method based on Bi-GRU and self-attention was proposed. First of all, the malicious code file was converted into the byte stream sequences of the same length, with each byte element (ranging from [0x00, 0xff]) represented by one-hot encoding. The features of the sequences were fully learnt by Bi-GRU according to their contextual hidden states. The hidden states of all time steps were output. Then, the hidden states of malicious time steps were assigned with more weights by using the self-attention mechanism, and the linear weighted sum of the hidden states were taken as the deep feature representation of the sample sequence. Finally, the feature representation was input to the fully connected neural network layer and the softmax layer to output the prediction probability of the sample. According to the experimental results, the AR of the proposed method increased by 12.25%, and its FPR reduced by 66.42% compared with the sub-optimal results. Thus, the method was feasible.
Emojinating: Hooked Beings

Emoji are pictorial symbols encoded in the Unicode Standard and thus available in most of writing systems. The popularity of emoji is constantly increasing among both users and brands, used to represent more concepts each year. Due to the connection between their pictorial representation and associated semantic knowledge, they can be computationally exploited. Emojinating is a co-creative system that produces visual representation of concepts by visually blending existing emoji. This paper presents an installation that offers a sneak peek at the different stages of the co-creative process: co-creation, production and interpretation. It has a dual nature, being composed of videos that show the interaction between artist and computer, and a set of four finished artworks under the theme "Addictive behaviors in the digital Era".