Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous and Multidimensional Clairvoyant Dynamic Bin Packing for Virtual Machine Placement

Although the public cloud still occupies the largest portion of the total cloud infrastructure, the private cloud is attracting increasing interest because of its better security and privacy control. According to previous research, a high upfront cost is among the most serious challenges associated with private cloud computing. Virtual machine placement (VMP) is a critical operation for cloud computing, as it improves performance and reduces cost. Extensive VMP methods have been researched, but few have been designed to reduce the upfront cost of private clouds. To fill this gap, in this paper, a heterogeneous and multidimensional clairvoyant dynamic bin packing (CDBP) model, in which the scheduler can conduct more efficient scheduling with additional time information to reduce the size of the datacenter and, thereby, decrease the upfront cost, is applied. An innovative branch-and-bound algorithm with the divide-and-conquer strategy (DCBB) is proposed to reduce the number of servers (#servers), with fast processing speed. In addition, some algorithms based on first fit (FF) and the ant colony system (ACS) are modified to apply them to the CDBP model. Experiments are conducted on generated and real-world data to check the performance and efficiency of the algorithms. The results confirm that the DCBB can make a tradeoff between performance and efficiency and also achieves a much faster convergence speed than that of other search-based algorithms. Furthermore, the DCBB yields the optimal solution under real-world workloads in much less runtime (by an order of magnitude) than required by the original branch-and-bound (BB) algorithm.
Synthesis of Differentiable Functional Programs for Lifelong Learning

We present a neurosymbolic approach to the lifelong learning of algorithmic tasks that mix perception and procedural reasoning. Reusing highlevel concepts across domains and learning complex procedures are two key challenges in lifelong learning. We show that a combination of gradientbased learning and symbolic program synthesis can be a more effective response to these challenges than purely neural methods. Concretely, our approach, called HOUDINI, represents neural networks as strongly typed, end-to-end differentiable functional programs that use symbolic higher-order combinators to compose a library of neural functions. Our learning algorithm consists of: (1) a program synthesizer that performs a type-directed search over programs in this language, and decides on the library functions that should be reused and the architectures that should be used to combine them; and (2) a neural module that trains synthesized programs using stochastic gradient descent. We evaluate our approach on three algorithmic tasks. Our experiments show that our type-directed search technique is able to significantly prune the search space of programs, and that the overall approach transfers high-level concepts more effectively than monolithic neural networks as well as traditional transfer learning.
QuickEdit: Editing Text & Translations via Simple Delete Actions

We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing and relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change. Our model then generates a new sentence which reformulates the initial sentence by avoiding the words from the marked tokens. Our approach builds upon neural sequence-to-sequence modeling and introduces a neural network which takes as input a sentence along with deleted token markers. Our model is trained on translation bi-text by simulating post-edits. Our results on post-editing for machine translation and paraphrasing evaluate the performance of our approach. We show +11.4 BLEU with limited post-editing effort on the WMT-14 English-German translation task (25.2 to 36.6), which represents +5.9 BLEU over the post-editing baseline (30.7 to 36.6).
Knowledge Base Construction as a Pedagogical Activity

This study examined cognitive processes and outcomes associated with student knowledge base development. Sixty-nine grade eight students were randomly assigned to one of three groups: a knowledge base development (KBD) group, a problem-solving software group, and a control group. Those in the KBD group received relevant instruction and then worked in small teams to develop very simple expert systems for weather prediction for about sixteen hours. Students in the software group engaged in problem-solving activities using The Factory and Super Factory; control group students completed weather instrument projects. MANOVA results for several measures of cognitive skill gain and transfer found no differences between groups. But for those students who scored higher than the grand median on a standardized pretest of abstract reasoning, there were significant main effects favoring the KBD group on a formal reasoning test and a transfer task. A qualitative analysis of students' cognitive task strategies is presented.
Learning to score and summarize figure skating sport videos

This paper focuses on fully understanding the figure skating sport videos. In particular, we present a large-scale figure skating sport video dataset, which include 500 figure skating videos. On average, the length of each video is 2 minute and 50 seconds. Each video is annotated by three scores from nine different referees, i.e., Total Element Score(TES), Total Program Component Score (PCS), and Total Deductions(DED). The players of this dataset come from more than 20 different countries. We compare different features and models to predict the scores of each video. We also derive a video summarization dataset of 476 videos with the ground-truth video summary produced from the great shot. A reinforcement learning based video summarization algorithm is proposed here; and the experiments show better performance than the other baseline video summarization algorithms.
An All-Memristor Deep Spiking Neural Network: A Step Towards Realizing the Low Power, Stochastic Brain

Deep 'Analog Artificial Neural Networks' (ANNs) perform complex classification problems with remarkably high accuracy. However, they rely on humongous amount of power to perform the calculations, veiling the accuracy benefits. The biological brain on the other hand is significantly more powerful than such networks and consumes orders of magnitude less power, indicating us about some conceptual mismatch. Given that the biological neurons communicate using energy efficient trains of spikes, and the behavior is non-deterministic, incorporating these effects in deep neural networks may drive us few steps towards a more realistic neuron. In this work, we propose how the inherent stochasticity of nano-scale resistive devices can be harnessed to emulate the functionality of a spiking neuron that can be incorporated in a deep Spiking Neural Networks (SNN). At the algorithmic level, we propose how the training can be modified to convert an ANN to an SNN while supporting the stochastic activation function offered by these devices. We devise circuit architectures to incorporate stochastic memristive neurons along with memristive crossbars which perform the functionality of the synaptic weights. We tested the proposed All Memristor deep SNN for image classification and observed only about 1% degradation in accuracy with the deep analog ANN baseline after incorporating the circuit and device related non-idealities. We witnessed that the network is robust to variations and consumes ~x11 less energy than its CMOS counterpart.
MD-ART: a test case generation method without test oracle problem

Adaptive random testing (ART), as an improved random testing method, preserves the advantages of traditional random test method and overcomes the blindness of traditional random testing method. But it is usually not easy to validate the correctness of the output, except for some special test cases. In other words, the test oracle problem is unresolved. In this research, we introduced metamorphic testing (MT) and metamorphic distance into ART, which is called metamorphic distance based ART (MD-ART) to provide the test oracle. The results of primary experiment results show that MD-ART performs better than traditional MT and ART not only in test effectiveness but also in test efficiency and test coverage.
Dynamic Trajectory Model for Analysis of Traffic States using DPMM

Appropriate modeling of a surveillance scene is essential while analyzing and detecting anomalies in road traffic. Learning usual paths can provide much insight into road traffic situation and to identify abnormal routes taken by commuters/vehicles in a traffic scene. If usual traffic paths are learned in a nonparametric way, manual effort of marking the roads can be avoided. We propose an unsupervised and nonparametric method to learn frequently used paths from the tracks of moving objects in $\Theta(kn)$ time, where $k$ is the number of paths and $n$ represents the number of tracks. In the proposed method, temporal correlation of the moving objects is taken into consideration to make the clustering meaningful using Temporally Incremental Gravity Model-Dynamic Trajectory Model (TIGM-DTM). In addition, the scene learning is based on distance, thus making it realistically intuitive in estimating the model parameters. Experimental validation reveals that the proposed method can learn a scene quickly without knowing the number of paths ($k$). We have compared the results with mean shift and DBSCAN. Further, we extend the model to represent states of a scene that can be used for taking timely actions. We have applied the model to understand its effectiveness in other domain.
Energy-relaxed Wassertein GANs(EnergyWGAN): Towards More Stable and High Resolution Image Generation

Recently, generative adversarial networks (GANs) have achieved great impacts on a broad number of applications, including low resolution(LR) image synthesis. However, they suffer from unstable training especially when image resolution increases. To overcome this bottleneck, this paper generalizes the state-of-the-art Wasserstein GANs (WGANs) to an energy-relaxed objective which enables more stable and higher-resolution image generation. The beneﬁts of this generalization can be summarized in three main points. Firstly, the proposed EnergyWGAN objective guarantees a valid symmetric divergence serving as a more rigorous and meaningful quantitative measure. Secondly, EnergyWGAN is capable of searching a more faithful solu-tion space than the original WGANs without ﬁxing a speciﬁc k -Lipschitz constraint. Finally, the proposed EnergyWGAN offers a natural way of stacking GANs for high resolution image generation. In our experiments we not only demonstrate the stable training ability of the proposed EnergyW-GAN and its better image generation results on standard benchmark datasets, but also show the advantages over the state-of-the-art GANs on a real-world high resolution image dataset.
Training Better CNNs Requires to Rethink ReLU

With the rapid development of Deep Convolutional Neural Networks (DCNNs), numerous works focus on designing better network architectures (i.e., AlexNet, VGG, Inception, ResNet and DenseNet etc.). Nevertheless, all these networks have the same characteristic: each convolutional layer is followed by an activation layer, a Rectified Linear Unit (ReLU) layer is the most used among them. In this work, we argue that the paired module with 1:1 convolution and ReLU ratio is not the best choice since it may result in poor generalization ability. Thus, we try to investigate the more suitable convolution and ReLU ratio for exploring the better network architectures. Specifically, inspired by Leaky ReLU, we focus on adopting the proportional module with N:M (N$>$M) convolution and ReLU ratio to design the better networks. From the perspective of ensemble learning, Leaky ReLU can be considered as an ensemble of networks with different convolution and ReLU ratio. We find that the proportional module with N:M (N$>$M) convolution and ReLU ratio can help networks acquire the better performance, through the analysis of a simple Leaky ReLU model. By utilizing the proportional module with N:M (N$>$M) convolution and ReLU ratio, many popular networks can form more rich representations in models, since the N:M (N$>$M) proportional module can utilize information more effectively. Furthermore, we apply this module in diverse DCNN models to explore whether is the N:M (N$>$M) convolution and ReLU ratio indeed more effective. From our experimental results, we can find that such a simple yet effective method achieves better performance in different benchmarks with various network architectures and the experimental results verify that the superiority of the proportional module.
CryptoRec: Secure Recommendations as a Service

Recommender systems rely on large datasets of historical data and entail serious privacy risks. A server offering recommendations as a service to a client might leak more information than necessary regarding its recommendation model and training dataset. At the same time, the disclosure of the client's preferences to the server is also a matter of concern. Providing recommendations while preserving privacy in both senses is a difficult task, which often comes into conflict with the utility of the system in terms of its recommendation-accuracy and efficiency. Widely-purposed cryptographic primitives such as secure multi-party computation and homomorphic encryption offer strong security guarantees, but in conjunction with state-of-the-art recommender systems yield far-from-practical solutions. We precisely define the above notion of security and propose CryptoRec, a novel recommendations-as-a-service protocol, which encompasses a crypto-friendly recommender system. This model possesses two interesting properties: (1) It models user-item interactions in a user-free latent feature space in which it captures personalized user features by an aggregation of item features. This means that a server with a pre-trained model can provide recommendations for a client without having to re-train the model with the client's preferences. Nevertheless, re-training the model still improves accuracy. (2) It only uses addition and multiplication operations, making the model straightforwardly compatible with homomorphic encryption schemes.
Evaluating neural network explanation methods using hybrid documents and morphological prediction

We propose two novel paradigms for evaluating neural network explanations in NLP. The first paradigm works on hybrid documents, the second exploits morphosyntactic agreements. Neither paradigm requires manual annotations; instead, a relevance ground truth is generated automatically. In our experiments, successful explanations for Long Short Term Memory networks (LSTMs) were produced by a decomposition of memory cells (Murdoch & Szlam, 2017), while for convolutional neural networks, a gradient-based method by (Denil et al., 2014) works well. We also introduce LIMSSE, a substring-based extension of LIME (Ribeiro et al., 2016) that produces the most successful explanations in the hybrid document experiment.
Simultaneous compressive image recovery and deep denoiser learning from undersampled measurements

Compressive image recovery utilizes sparse image priors such as wavelet l1 norm, total-variation (TV) norm, or self-similarity to reconstruct good quality images from highly compressive samples. Recently, there have been some attempts to exploit data-driven image priors from massive amount of clean images in compressive image recovery such as LDAMP algorithm. By utilizing large-scale noiseless images for training deep neural network denoisers, LDAMP outperformed other conventional compressive image reconstruction methods. However, one drawback of LDAMP is that large-scale noiseless images must be acquired for deep learning based denoisers. In this article, we propose a method for simultaneous compressive image recovery and deep denoiser learning from undersampled measurements that enables compressive image recovery methods to use data-driven image priors when only large-scale compressive samples are available without ground truth images. By utilizing the structure of LDAMP and Stein’s Unbiased Risk Estimator (SURE) based deep neural network denoiser, we showed that our proposed methods were able to achieve better performance than other methods such as conventional BM3DAMP and LDAMP methods trained with the results of BM3D-AMP for training data and/or testing data for all cases with i.i. d. Gaussian random and coded diffraction measurement matrices at various compression ratios. We also investigated accurate noise level estimation methods in LDAMP for coded diffraction measurement matrix to train deep denoiser networks for high performance.
Network structure inhibits information cascades in heavy-tailed social networks

Information and ideas spread over social networks, with online social media platforms being a powerful medium to accelerate information flow. Characterising and understanding the role of network structure on this information flow is important in numerous fields, yet is limited by the availability of network data exhibiting a range of structural properties. It is therefore essential to use mathematical network models with tunable parameters to fully explore information cascade phenomena. We show through numerical simulations on a range of network types that underlying graph structure affects both the probability of information cascades, and explore the nature of these cascades. Using a paradigmatic threshold model of information cascades on simulated random networks, we show that network locality increases the vulnerability of connecting nodes, and hence the probability of global cascades. Conversely, we find that in heavy-tailed networks, global cascade probability is reduced by the presence of highly connected blocking nodes.
A Particle Swarm Optimization Approach for Routing in VLSI

The performance of very large scale integration (VLSI) circuits is depends on the interconnected routing in the circuits. In VLSI routing, wire sizing, buffer sizing, and buffer insertion are techniques to improve power dissipation, area usage, noise, crosstalk, and time delay. Without considering buffer insertion, the shortest path in routing is assumed having the minimum delay and better performance. However, the interconnect delay can be further improved if buffers are inserted at proper locations along the routing path. Hence, this paper proposes a heuristic technique to simultaneously find the optimal routing path and buffer location for minimal interconnect delay in VLSI based on particle swarm optimization (PSO). PSO is a robust stochastic optimization technique based on the movement and information sharing of swarms. In this study, location of doglegs is employed to model the particles that represent the routing solutions in VLSI. The proposed approach has a good potential in VLSI routing and can be further extended in future
Coded Computation Against Distributed Straggling Decoders for Gaussian Channels in C-RAN

The uplink via additive white Gaussian noise channels of a cloud radio access network (C-RAN) architecture is studied, where decoding at the cloud takes place over network function virtualization on commercial off-the-shelf servers. To mitigate the impact of straggling decoders, the cloud re-encodes the received frames via a linear code before distributing them to the decoding processors. In this paper, the computation rate is derived such that all the servers can recover the desired linear combinations of the messages with vanishing average error probability. It follows a computation of an upper bound on the decoding error probability of each server. Finally, two analytical upper bounds on the frame error rate (FER) as a function of the decoding latency are developed, in order to quantify the tradeoff between FER and decoding latency at the cloud.
Unsupervised Learning aids Prediction: Using Future Representation Learning Variantial Autoencoder for Human Action Prediction

The unsupervised Pretraining method has been widely used in aiding human action recognition. However, existing methods focus on reconstructing the already present frames rather than generating frames which happen in future.In this paper, We propose an improved Variantial Autoencoder model to extract the features with a high connection to the coming scenarios. Our framework lists as following: two steam 3D-convolution neural networks are used to extract both spatial and temporal information as latent variables. Then a resample method is introduced to create new normal distribution probabilistic latent variables and finally, the deconvolution neural network will use these latent variables generate next frames. Through this possess, we train the model to focus more on how to generate the future and thus it will extract the future high connected features. In the experiment stage, A large number of experiments on UT dataset reveal that future generation aids Prediction does improve the performance. Moreover, the Future Representation Learning Network reach a higher score than other methods when in half observation. This means that Future Representation Learning is better than the traditional Representation Learning and other state-of-the-art methods in solving the human action prediction problems to some extends.
Open-ended Exploration of the Program Repair Search Space with Mined Templates: the Next 8935 Patches for Defects4J

In this paper our goal is to perform an open-ended exploration of the program repair search space. Our idea is to collect the largest number of test-suite adequate patches, independently of whether they are fully correct or overfitting. For that, we design Cardumen, a repair approach based mined templates that has an ultra-large search space. We evaluate the capacity of Cardumen to discover test-suite adequate patches (aka plausible patches) over the 356 real bugs from Defects4J by Just et al. (2014). Cardumen finds 8935 patches over 77 bugs of Defects4J. This is the largest number of automatically synthesized patches ever reported, all patches being available in an open- science repository. Moreover, Cardumen identifies 8 unique patches, that are patches for Defects4J bugs that were never repaired in the whole history of program repair.