Miracle-dz/UniFAR-FTUs huggingface dataset:
DART: A Framework for Determining Approximation Levels in an Approximable Memory Hierarchy

In this article, we propose a framework for determining approximation levels of approximable memories in a memory hierarchy for executing error resilient applications. The framework aims at optimizing the configuration for employing approximate memories in a computing system. It is based on considering data footprints at different memory hierarchy levels and an expected output quality to determine the amount of approximations at each memory hierarchy level. The problem of finding a suitable memory approximation configuration is performed using a branch-and-bound algorithm considering all possible memory approximation arrangements. The best configuration leading to the lowest power consumption when meeting the expected output quality is selected. The efficacy of the proposed framework for two memory hierarchies with different cache topologies is evaluated by comparing energy consumptions of approximate memories with those of the exact memory units in the memory hierarchy under different output accuracy level targets. For example, with 28 dB as a peak signal to noise ratio (PSNR) constraint, the study, which is performed for four image processing applications, indicates up to 54% and 22% power consumption improvements for the SRAM cache and the DRAM memory, respectively.
The effectiveness of using Malay affixes for handling unknown words in unsupervised HMM POS tagger

The challenge in unsupervised Hidden Markov Model (HMM) training for a POS tagger is that the training depends on an untagged corpus; the only supervised data limiting possible tagging of words is a dictionary. A morpheme-based POS guessing algorithm has been introduced to assign unknown words’ probable tags based on linguistically meaningful affixes. Therefore, the exact morphemes of pre-fixes, suffixes and circumfixes in the agglutinative Malay language is examined before giving tags to unknown words. The algorithm has been integrated into HMM tagger which uses HMM trained parameters for tagging new sentences. However, for unknown words their parameters are absent. Therefore, the algorithm applies two methods for assigning unknown words’ emission to HMM tagger, first is based on uniform distribution of all possible tags; and second, is based on marginal proportionate distribution of tags. The effective method is proven to be using morpheme-based POS guessing with unknown word emissions substituted by a value proportionate to the marginal distribution of tags.
System-on-a-chip (SoC) FPGA-based Hardware Acceleration for Green Crop Segmentation

With the development of artificial intelligence technology, agricultural robot plays a significantly important role for agricultural intelligence. Crop segmentation is a critical and fundamental pre-processing step for agricultural robot navigation and crop growth monitoring. Although there are several crop segmentation methods, they cannot meet the online real-time requirement for agricultural robot. In view of this, a real-time crop segmentation system implemented on a SoC-FPGA is first proposed in this paper, which contains image loading module, segmentation algorithm module and image display module. As a core module, segmentation algorithm module consists of greenness identification, thresholding operation and morphological operation. Its hardware implementation adopted a parallel pipeline architecture to enhance real-time performance. The proposed system is implemented on a Xilinx Zynq-7000 FPGA and the experimental results showed that it can achieve a frame rate of 60 frames per second for the image resolution of 1920×1080 with the average accuracy of 91%.
A Comparison of Outcomes of Treatment with Thai Traditional Massage and Ultrasound in Patients with Cervical Myofascial Pain Syndrome

Rationale: Patients with cervical pain caused by myofascial pain syndrome (MPS) are commonly found in clinics. Thai traditional massage and ultrasound have been used as treatment methods for MPS. However, little is known about the effects of those methods. Objective: To compare outcomes of treatment between Thai traditional massage and ultrasound in patients with cervical pain from MPS. Method: From 2005 to 2007, 44 patients with cervical pain from MPS participated in this study. The subjects were randomly divided into two groups and treated in three sessions a week for four weeks. The first group was treated with ultrasound and the second group with Thai massage. Cervical range of motion (CROM), pain levels using visual analog scale (VAS), functional rating index (FRI) before and after treatments, and patients’ satisfaction after treatments were measured. Comparative analyses of data before and after the treatment used were subjected to paired t-test and Wilcoxon signed-rank test. Between groups were analyzed by independent t-test and Mann-Whitney U-test, with the level of significance being 0.05. Result: CROM values were increased significantly after the Thai massage and ultrasound treatments (p = 0.001). VAS and FRI decreased significantly after both treatments (p=0.001). There was no difference on CROM and VAS in comparison between the two treatments. FRI in the Thai massage group was better than in the ultrasound group (p = 0.044). In addition, patients’ satisfaction was significantly greater in the Thai massage group (p = 0.014). Summary: Both Thai massage and ultrasound can significantly increase CROM and decrease pain scores and pain during daily living activities in patients with cervical MPS. Thai massage enhances their ability to perform daily activities and produces greater patient satisfaction when compared with ultrasound. Therefore, we encourage the use of Thai massage in patients with cervical pain caused by MPS. Key words: Thai massage, traditional massage, ultrasound, cervical myofascial pain syndrome
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation

Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context for translation. In this paper, we propose a hierarchical model to learn the global context for document-level neural machine translation (NMT). This is done through a sentence encoder to capture intra-sentence dependencies and a document encoder to model document-level inter-sentence consistency and coherence. With this hierarchical architecture, we feedback the extracted global document context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context. In addition, since large-scale in-domain document-level parallel corpora are usually unavailable, we use a two-step training strategy to take advantage of a large-scale corpus with out-of-domain parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability. Experimental results on several benchmark corpora show that our proposed model can significantly improve document-level translation performance over several strong NMT baselines.
Fast and flexible tracepoints in x86

Tracing is often the most effective technique for analyzing the performance of complex multithreaded applications. This paper presents an improvement on existing techniques for dynamic tracepoint insertion. To add a tracepoint, the technique inserts a jump at the tracing point, possibly replacing several shorter instructions. This jump embeds trap instructions inside its offset at the address of every replaced instruction. This makes the jump thread safe if any thread is about to execute a replaced instruction. It also makes it jump safe if a jump landing pad is at one of the replaced instructions. In both cases, a trap will be raised, and the thread can be redirected to the out‐of‐line equivalent instruction. The use of a jump instead of a trap to execute the tracepoint improves the performance of the execution. It also adds the flexibility to place the tracepoint at almost any instruction, since multiple instructions can be replaced atomically and safely. The downside of this technique is the increased memory usage, since it requires unaligned allocations with high external fragmentation.
Person-Specific Joy Expression Synthesis with Geometric Method

Smiling has a psychiatric effect in emotional state and may hold tremendous potential for clinical remediation in psychiatric disorders. A few researchers in image synthesis work on acting on the emotional state of subjects by automatically deforming their faces to synthesize joyful expression. However, to generate these expressions they apply the same deformation for the subjects while each person smiles differently. In this paper, we head towards a personalized synthesis of the joy expression. We have studied the trajectories of the face landmarks during a smile on the CK, Oulu-CASIA and MMI databases. The obtained results show that the smile is personal, straight and it occurs in a different way for each person. That is why we propose a system that can photo-realistically transform in real-time a detected face into a personalized joyful one using a geometric method. Both visual fidelity and a statistical study demonstrate that our person-specific method can generate personalized joy expressions closer to the ground truth than two non-personal state-of-the-art approaches.
Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas

Most existing relation extraction approaches exclusively target binary relations, and n-ary relation extraction is relatively unexplored. Current state-of-the-art n-ary relation extraction method is based on a supervised learning approach and, therefore, may suffer from the lack of sufficient relation labels. In this paper, we propose a novel approach to cross-sentence n-ary relation extraction based on universal schemas. To alleviate the sparsity problem and to leverage inherent decomposability of n-ary relations, we propose to learn relation representations of lower-arity facts that result from decomposing higher-arity facts. The proposed method computes a score of a new n-ary fact by aggregating scores of its decomposed lower-arity facts. We conduct experiments with datasets for ternary relation extraction and empirically show that our method improves the n-ary relation extraction performance compared to previous methods.
Classifying Hate Speech Using a Two-Layer Model

ABSTRACT Social media and other online sites are being increasingly scrutinized as platforms for cyberbullying and hate speech. Many machine learning algorithms, such as support vector machines, have been adopted to create classification tools to identify and potentially filter patterns of negative speech. While effective for prediction, these methodologies yield models that are difficult to interpret. In addition, many studies focus on classifying comments as either negative or neutral, rather than further separating negative comments into subcategories. To address both of these concerns, we introduce a two-stage model for classifying text. With this model, we illustrate the use of internal lexicons, collections of words generated from a pre-classified training dataset of comments that are specific to several subcategories of negative comments. In the first stage, a machine learning algorithm classifies each comment as negative or neutral, or more generally target or nontarget. The second stage of model building leverages the internal lexicons (called L2CLs) to create features specific to each subcategory. These features, along with others, are then used in a random forest model to classify the comments into the subcategories of interest. We demonstrate our approach using two sets of data. Supplementary materials for this article are available online.
Submodular Function Minimization with Noisy Evaluation Oracle

This paper considers submodular function minimization with \textit{noisy evaluation oracles} that return the function value of a submodular objective with zero-mean additive noise. For this problem, we provide an algorithm that returns an $O(n^{3/2}/\sqrt{T})$-additive approximate solution in expectation, where $n$ and $T$ stand for the size of the problem and the number of oracle calls, respectively. There is no room for reducing this error bound by a factor smaller than $O(1/\sqrt{n})$. Indeed, we show that any algorithm will suffer additive errors of $\Omega(n/\sqrt{T})$ in the worst case. Further, we consider an extended problem setting with \textit{multiple-point feedback} in which we can get the feedback of $k$ function values with each oracle call. Under the additional assumption that each noisy oracle is submodular and that $2 \leq k = O(1)$, we provide an algorithm with an $O(n/\sqrt{T})$-additive error bound as well as a worst-case analysis including a lower bound of $\Omega(n/\sqrt{T})$, which together imply that the algorithm achieves an optimal error bound up to a constant.
Multi Source Feedback Among Physicians, is it Effective? A Rapid Evidence Assessment

The board of directors of two Dutch hospitals asked the authors to research the available evidence regarding the ‘effectiveness of Multi Source Feedback (MSF) among physicians in hospitals in order to increase performance improvement’. To answer this, the authors ran a systematic review using the Rapid Evidence Assessment methodology. This paper presents the available research evaluating the effectiveness of MSF and variables that are expected to have an impact on the effectiveness of MSF among physicians.
NAT: Neural Architecture Transformer for Accurate and Compact Architectures

Designing effective architectures is one of the key factors behind the success of deep neural networks. Existing deep architectures are either manually designed or automatically searched by some Neural Architecture Search (NAS) methods. However, even a well-searched architecture may still contain many non-significant or redundant modules or operations (e.g., convolution or pooling), which may not only incur substantial memory consumption and computation cost but also deteriorate the performance. Thus, it is necessary to optimize the operations inside an architecture to improve the performance without introducing extra computation cost. Unfortunately, such a constrained optimization problem is NP-hard. To make the problem feasible, we cast the optimization problem into a Markov decision process (MDP) and seek to learn a Neural Architecture Transformer (NAT) to replace the redundant operations with the more computationally efficient ones (e.g., skip connection or directly removing the connection). Based on MDP, we learn NAT by exploiting reinforcement learning to obtain the optimization policies w.r. t. different architectures. To verify the effectiveness of the proposed strategies, we apply NAT on both hand-crafted architectures and NAS based architectures. Extensive experiments on two benchmark datasets, i.e., CIFAR-10 and ImageNet, demonstrate that the transformed architecture by NAT significantly outperforms both its original form and those architectures optimized by existing methods.
Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration

In multi-turn dialogue, utterances do not always take the full form of sentences. These incomplete utterances will greatly reduce the performance of open-domain dialogue systems. Restoring more incomplete utterances from context could potentially help the systems generate more relevant responses. To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K is collected and manually labeled with the explicit relation between an utterance and its context. We also propose a “pick-and-combine” model to restore the incomplete utterance from its context. Experimental results demonstrate that the annotated dataset and the proposed approach significantly boost the response quality of both single-turn and multi-turn dialogue systems.
Health and Nutrition Secrets That Can Save Your Life

Dr. Russell Blaylock’s latest magnum opus is arranged in two major sections, either one alone more than worth the book price. It is a welcome addition to his earlier work on excitotoxins. Superb chapters cover essentially every aspect of health and nutrition; brain and body protection against toxins, injury, and disease; and even defense against bioterrorism. His discussion on “protecting your brain” is an outstanding read, and his chapter on “surviving a terrorist attack” is timely indeed. He covers causes of degenerative diseases, including the bad effects of free radicals and the benefits of certain minerals, vitamins, and other more powerful antioxidants; nutrition, genes, and genetic switches; the danger of mercury from various sources; the effect of fluoride from drinking water, toothpaste, and other sources; other toxic metals to avoid; vaccination hazards; toxic food additives; pesticides and other harmful chemicals; and causes of arteriosclerosis, stroke, heart attack, and other diseases of aging and how to prevent them. Dr. Blaylock offers one of the best discussions on the pathophysiology of free radical formation and the damage it causes various human cells and tissues, particularly in the central nervous system. His description of the mechanisms of cellular injury is clear and persuasive. So is his cited research and explanation for clinical protection provided by proper nutrition and the human antioxidant network, including vitamins, enzymes, and other antioxidants such as glutathione and alpha lipoic acid. For all this, Dr. Blaylock has only gathered material and drawn medical conclusions that, although reflecting the most advanced state of medical knowledge, cannot be written in stone. This book, like any cutting-edge medical text, requires a caution for the uninitiated, whether health enthusiast or unwary clinician. Remember, the absolute amount at which any substance becomes toxic is 1 Book Reviews
Multivariate time series classification based on M-histograms and multi-view learning

Univariate time series (UTS) classification has been reported in several papers, where various efficient models have been proposed. Such models are often inadequate for multivariate time series (MTS) classification. MTS emerged with the multiplication of sensors that record large amounts of high-dimensional data, characterized by several dimensions , variable lengths, noise and correlations between dimensions. MTS classification is a challenging task, and few works have been devoted to complex data. In this paper, we propose a novel subspace model that combines M-histograms and multi-view learning together with an ensemble learning technique to handle MTS classification task. The M-histograms is a statistical tool, efficient for data visualization, that can reveal mutual information from different dimensions. Thus it can be suitable for MTS data encoding. Multi-view learning is known as data integration from multiple feature sets, as it is the case with MTS data, and multiple views also provide complementary information. The new combined model provides a MTS encoding that can outperform other time series encoding such as Symbolic Aggregate approXimation (SAX) w.r. t. to the experimental comparison that was conducted. We also benchmark our method with some state-of-art methods devoted to MTS, and discuss the results obtained and the main properties of our model.
Spectral-Spatial Classification from Multi-Sensor Compressive Measurements Using Superpixels

Compressive spectral imaging (CSI) acquires coded projections of a spectral image by performing a modulation of the data cube followed by a spectral-wise integration. To avoid the spectral image reconstruction procedure, this paper proposes a classification approach that extracts features directly from multi-sensor CSI measurements. Particularly, the proposed method obtains the features by considering the spectral information extracted from Hyperspectral CSI measurements, and the local spatial information extracted by clustering the Multispectral CSI measurements using a superpixel algorithm. This approach is evaluated on Pavia University and Salinas Valley datasets. Extensive simulations show that considering the local spatial information boosts the overall accuracy up to 3% in comparison with traditional approaches that only uses the spectral information. Furthermore, the computation time of the approach that reconstructs, fuses and classifies takes approximately 87.43 [s], while classifying directly from multi-sensor compressive measurements takes only 0.74 [s], achieving similar classification results.
Multi-Person Pose Estimation via Learning Feature Integration

Human pose estimation is a fundamental but challenging task in computer vision. Although batch normalization is widely used for deep learning, feature extraction in deep convolutional neural networks (DCNN) is still not well explored. In this work, we propose a pose encoding module (PEM) to enhance the learning ability and generalization ability of feature extraction. Given input images, PEM integration instance normalization and batch normalization, combining them in an appropriate way to learn to capture and eliminate appearance changes while maintaining the distinction between learning features can be seen as an integration and adjustment of global information. In addition, we use a simple and efficient up-sampling strategy to recover high-resolution representations for predicting more accurate human keypoint heatmaps, which has achieved better performance than the average network in recent missions. We studied our approach on a standard benchmark for human pose estimation.
Serious game for waste sorting on interactive tabletop with tangible objects: exploratory study

In the field of microbiology training, in which contaminated products must be handled and recycled, there are new needs in order to reduce risks and costs. New advances in Tangible interaction and serious game domains can provide solutions to answer these needs. We propose a serious game relating to waste sorting. It is available on a tangible tabletop equipped with RFID technology. The design and realisation using tangibility and gamification concepts are presented. An exploratory study with students in microbiology and experts is described. The first results obtained are promising. They show the potentiality of the approach.
Computational advantages of dopaminergic states for decision making

Dopamine’s (DA) role in the striatal direct (D1) and indirect (D2) pathways suggests a more complex system than that captured by standard reinforcement learning (RL) models. The Opponent Actor Learning (OpAL) model (Collins & Frank, 2014) presented a more biologically plausible and interactive account, incorporating interactive incentive motivation and learning effects of dopamine in one dual-actor framework. In OpAL, DA modulates not only learning but the influence of each actor on decision making, where the two actors specialize in encoding the benefits and costs of actions (D1 and D2 pathways, respectively). While OpAL accounts for a wide range of DA effects on learning and choice, formal analysis of the normative advantage of allowing the motivational state (the level of dopamine at choice) to be optimized is still needed. We present simulations which suggest a computational benefit to high motivational states in ”rich” environments where all action options have high probability of reward; conversely, lower motivational states have computational benefit in ”lean” environments. We show how online modulation of motivational states according to the environment value or the inference about the appropriate latent state of the environment confers a benefit beyond that afforded by classic RL in learning and risk paradigms. These simulations offer a clue as to the normative function of the biology of RL that differs from the standard model-free RL algorithms in computer science.