Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Head Attention with Disagreement Regularization

Multi-head attention is appealing for the ability to jointly attend to information from different representation subspaces at different positions. In this work, we introduce a disagreement regularization to explicitly encourage the diversity among multiple attention heads. Specifically, we propose three types of disagreement regularization, which respectively encourage the subspace, the attended positions, and the output representation associated with each attention head to be different from other heads. Experimental results on widely-used WMT14 English-German and WMT17 Chinese-English translation tasks demonstrate the effectiveness and universality of the proposed approach.
Waterwheel: Realtime Indexing and Temporal Range Query Processing over Massive Data Streams

Massive data streams from sensors in Internet of Things (IoT) and smart devices with Global Positioning System (GPS) are now flooding to database systems for further processing and analysis. The capability of real-time retrieval from both fresh and historical data turns out to be the key enabler to the real world applications in smart manufacturing and smart city utilizing these data streams. In this paper, we present a simple and effective distributed solution to achieve millions of tuple insertions per second and ad-hoc temporal range query processing in milliseconds. To this end, we propose a new data partitioning scheme that takes advantage of the workload characteristics and avoids expensive global data merging. Furthermore, to resolve the throughput bottleneck, we adopt a template-based index method to skip unnecessary index structure adjustments over the relatively stable distribution of incoming tuples. To parallelize data insertion and query processing, we propose an efficient dispatching mechanism and effective load balancing strategies to fully utilize computational resources in a workload-aware manner. On both synthetic and real workloads, our solution consistently outperforms state-of-the-art open-source systems by at least an order of magnitude.
Efficient VLSI Implementation of a Sequential Finite Field Multiplier Using Reordered Normal Basis in Domino Logic

In this paper, a high-speed power-efficient VLSI implementation of a finite field multiplier in GF( $2^{m}$ ) is presented. The proposed design has a serial-in parallel-out architecture and performs the multiplication operation using a reordered normal basis. The basic idea is to implement the main building block of the multiplier in domino logic to reduce the critical path delay. Reduction in dynamic power consumption is achieved by limiting the contention current between the keeper transistor and the pull-down network at the beginning of the evaluation phase by employing a new keeper control circuit. The semicustom layout of the multiplier was realized in 65-nm CMOS technology. The post place-and-route simulations showed that the multiplier can perform multiplication correctly up to a clock rate of 3.85 GHz and consumes marginally less power than the static CMOS counterpart (also implemented with custom placement and route). The size of the multiplier is currently recommended by the National Institute of Standards and Technology for binary field multiplication in elliptic curve cryptography. The proposed design methodology can also be used in the implementation of similar finite field multipliers possessing regular architectures.
Sampling-based Tube Following for Redundant, Planar Robotic Manipulators

Ahstract- This paper introduces a global, sampling-based motion planning approach to the tube following problem for redundant robot manipulators. Tube following is a motion planning problem where an under-defined end-effector path is given. We introduce a novel combination of existing task space and redundant configuration space sampling techniques. These techniques are applied to the tube following problem, comparing three different sampling techniques: uniform grid, uniform random and Halton sampling. In addition, an incremental sampling technique is proposed, specifically for tube following problems. Eventually, iterative grid refining is used to locally optimise path cost. Experimental results demonstrate the clear potential of these techniques to achieve fully automatic, collision-free motion planning for tube following with redundant robot manipulators. Our planning software is publicly available.
Two-Wheel Cane for Walking Assistance

A hardware design and controller of two-wheel cane has been proposed in this paper. Our two-wheel cane has been designed to assist users in maintaining balance as they move. We validated the performance of the controller designed to stabilize the inverted pendulum’s balance based on the test results with the designed robot modeling. Experimental results show that two-wheel cane can be most useful to support elderly persons maintain balance.
Combining Temporal Aspects of Dynamic Networks with Node2Vec for a more Efficient Dynamic Link Prediction

In many real-life applications it is crucial to be able to, given a collection of link states of a network in a certain time period, accurately predict the link state of the network at a future time. This is known as dynamic link prediction, which compared to its static counterpart is more complex, as capturing the temporal characteristics is a non-trivial task. This explains while still majority of today's research in network representation learning focuses on static setting ignoring temporal information. In this work, we focus on one such case and aim at extending node2vec, representation learning method successfully applied for static link prediction, to a dynamic setup. This extended method is applied and validated on several real-life networks with different properties. Results show that taking into account dynamic aspect outperforms static approach. Additionally, based on the network properties, recommendations are given for the node2vec parameters.
A Comprehensive Performance Evaluation of Modern In-Memory Indices

Due to poor cache utilization and latching contention, the B-tree like structures, which have been heavily used in traditional databases, are not suitable for modern in-memory databases running over multi-core infrastructure. To address the problem, several in-memory indices, such as FAST, Masstree, BwTree, ART and PSL, have recently been proposed, and they show good performance in concurrent settings. Given the various design choices and implementation techniques being adopted by these indices, it is therefore important to understand how these techniques and properties actually affect the indexing performance. To this end, we conduct a comprehensive performance study to compare these indices from multiple perspectives, including query throughput, scalability, latency, memory consumption as well as cache/branch miss rate, using various query workloads with different characteristics. Our results indicate that there is no one-size-fits-all solution. For example, PSL achieves better query throughput for most settings, but occupies more memory space and can incur a large overhead in updating the index. Nevertheless, the huge performance gain renders the exploitation of modern hardware features indispensable for modern database indices.
The design and evaluation of a gestural keyboard for entering programming code on mobile devices

We present the design and the evaluation of a soft keyboard aimed at facilitating the input of programming code on mobile devices equipped with touch screens, such as tablets and smartphones. Besides the traditional tap on a key interaction, the keyboard allows the user to draw gestures on top of it. The gestures correspond to shortcuts to enter programming statements/constructs or to activate specific keyboard sub-layouts. The keyboard was compared in a user study to a traditional soft keyboard with a QWERTY layout and to another state-of-art keyboard designed for programming. The results show a significant advantage for our design in terms of speed and gesture per characters.
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection

Emotion recognition in conversations is crucial for building empathetic machines. Present works in this domain do not explicitly consider the inter-personal influences that thrive in the emotional dynamics of dialogues. To this end, we propose Interactive COnversational memory Network (ICON), a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories. Such memories generate contextual summaries which aid in predicting the emotional orientation of utterance-videos. Our model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets.
One size does not fit all: an empirical study of containerized continuous deployment workflows

Continuous deployment (CD) is a software development practice aimed at automating delivery and deployment of a software product, following any changes to its code. If properly implemented, CD together with other automation in the development process can bring numerous benefits, including higher control and flexibility over release schedules, lower risks, fewer defects, and easier on-boarding of new developers. Here we focus on the (r)evolution in CD workflows caused by containerization, the virtualization technology that enables packaging an application together with all its dependencies and execution environment in a light-weight, self-contained unit, of which Docker has become the de-facto industry standard. There are many available choices for containerized CD workflows, some more appropriate than others for a given project. Owing to cross-listing of GitHub projects on Docker Hub, in this paper we report on a mixed-methods study to shed light on developers' experiences and expectations with containerized CD workflows. Starting from a survey, we explore the motivations, specific workflows, needs, and barriers with containerized CD. We find two prominent workflows, based on the automated builds feature on Docker Hub or continuous integration services, with different trade-offs. We then propose hypotheses and test them in a large-scale quantitative study.
Collective Event Detection via a Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms

Traditional approaches to the task of ACE event detection primarily regard multiple events in one sentence as independent ones and recognize them separately by using sentence-level information. However, events in one sentence are usually interdependent and sentence-level information is often insufficient to resolve ambiguities for some types of events. This paper proposes a novel framework dubbed as Hierarchical and Bias Tagging Networks with Gated Multi-level Attention Mechanisms (HBTNGMA) to solve the two problems simultaneously. Firstly, we propose a hierachical and bias tagging networks to detect multiple events in one sentence collectively. Then, we devise a gated multi-level attention to automatically extract and dynamically fuse the sentence-level and document-level information. The experimental results on the widely used ACE 2005 dataset show that our approach significantly outperforms other state-of-the-art methods.
Outfit Recommender System

The online apparel retail market size in the United States is worth about seventy-two billion US dollars. Recommender systems on retail websites generate a lot of this revenue. Thus, improving recommender systems can increase their revenue. Traditional recommendations for clothes consisted of lexical methods. However, visual-based recommendations have gained popularity over the past few years. This involves processing a multitude of images using different image processing techniques. In order to handle such a vast quantity of images, deep neural networks have been used extensively. With the help of fast Graphics Processing Units, these networks provide results which are extremely accurate, within a small amount of time. However, there are still ways in which recommendations for clothes can be improved. We propose an event-based clothing recommender system which uses object detection. We train a model to identify nine events/scenarios that a user might attend: White Wedding, Indian Wedding, Conference, Funeral, Red Carpet, Pool Party, Birthday, Graduation and Workout. We train another model to detect clothes out of fifty-three categories of clothes worn at the event. Object detection gives a mAP of 84.01. Nearest neighbors of the clothes detected are recommended to the user.
Computational Electromagnetics in Service of Polarimetric Phased Array Radar Calibration - What Causes the Differential Phase bias?

Calibration of the planar polarimetric phased array radar for weather purposes is angle dependent. Yet measuring of all parameters required to calibrate polarimetric variables is impractical for large radar antennas. Here, an alternative computational electromagnetic modeling approach is considered. We use a full size demonstrator of larger polarimetric radar for evaluation of polarimetric biases expected in the larger array. Particularly, we attempt identification of the bias sources in the differential phase measurements.
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting

Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected linguistic variables. However, the selection of these variables is theory-driven and itself insensitive to change. We use Doc2Vec on a corpus of 16.8M anonymous online posts in the German-speaking area to learn continuous document representations of cities. These representations capture continuous regional linguistic distinctions, and can serve as input to downstream NLP tasks sensitive to regional variation. By incorporating geographic information via retrofitting and agglomerative clustering with structure, we recover dialect areas at various levels of granularity. Evaluating these clusters against an existing dialect map, we achieve a match of up to 0.77 V-score (harmonic mean of cluster completeness and homogeneity). Our results show that representation learning with retrofitting offers a robust general method to automatically expose dialectal differences and regional variation at a finer granularity than was previously possible.
Modeling Localness for Self-Attention Networks

Self-attention networks have proven to be of profound value for its strength of capturing global dependencies. In this work, we propose to model localness for self-attention networks, which enhances the ability of capturing useful local context. We cast localness modeling as a learnable Gaussian bias, which indicates the central and scope of the local region to be paid more attention. The bias is then incorporated into the original attention distribution to form a revised distribution. To maintain the strength of capturing long distance dependencies while enhance the ability of capturing short-range dependencies, we only apply localness modeling to lower layers of self-attention networks. Quantitative and qualitative analyses on Chinese-English and English-German translation tasks demonstrate the effectiveness and universality of the proposed approach.
Symbolic execution with existential second-order constraints

Symbolic execution systematically explores program paths by solving path conditions --- formulas over symbolic variables. Typically, the symbolic variables range over numbers, arrays and strings. We introduce symbolic execution with existential second-order constraints --- an extension of traditional symbolic execution that allows symbolic variables to range over functions whose interpretations are restricted by a user-defined language. The aims of this new technique are twofold. First, it offers a general analysis framework that can be applied in multiple domains such as program repair and library modelling. Secondly, it addresses the path explosion problem of traditional first-order symbolic execution in certain applications. To realize this technique, we integrate symbolic execution with program synthesis. Specifically, we propose a method of second-order constraint solving that provides efficient proofs of unsatisfiability, which is critical for the performance of symbolic execution. Our evaluation shows that the proposed technique (1) helps to repair programs with loops by mitigating the path explosion, (2) can enable analysis of applications written against unavailable libraries by modelling these libraries from the usage context.
Persistent Community Search in Temporal Networks

Community search is a fundamental graph mining task. Unfortunately, most previous community search studies focus mainly on identifying communities in a network without temporal information. In this paper, we study the problem of finding persistent communities in a temporal network, in which every edge is associated with a timestamp. Our goal is to identify the communities that are persistent over time. To this end, we propose a novel persistent community model called (θ,⊺) community. We prove that the problem of identifying the maximum (θ,⊺) persistent k-core is NP-hard. To solve this problem, we propose a novel branch and bound algorithm with several carefully-designed pruning rules to find the maximum (θ,⊺)-persistent. We conduct k-cores efficiently. We conduct extensive experiments in several real-world temporal networks. The results demonstrate the efficiency, scalability, and effectiveness of the proposed solutions.
Identification of Cortical and Subcortical Correlates of Cognitive Performance in Multiple Sclerosis Using Voxel-Based Morphometry

Objective: Cognitive impairment is an important feature in multiple sclerosis (MS) and has been associated to several Magnetic Resonance Imaging (MRI) markers, but especially brain atrophy. However, the relationship between specific neuropsychological tests examining several cognitive functions and brain volumes has been little explored. Furthermore, because MS frequently damage subcortical regions, it may be an interesting model to examine the role of subcortical areas in cognitive functioning. Our aim was to identify correlations between specific brain regions and performance in neuropsychological tests evaluating different cognitive functions in a large series of patients with MS. Methods: A total of 375 patients were evaluated with a comprehensive neuropsychological battery and with MRI. Voxel-based morphometry was conducted to analyse the correlation between cognitive performance and gray matter damage, using Statistical Parametric Mapping with the toolboxes VBM8 and Lesion Segmentation Tool. Results: The following correlations were found: Corsi block-tapping test with right insula; Trail Making Test with caudate nucleus, thalamus, and several cortical regions including the posterior cingulate and inferior frontal gyrus; Symbol Digit Modalities Test with caudate nucleus, thalamus, posterior cingulate, several frontal regions, insula, and cerebellum; Stroop Color and Word Test with caudate nucleus and putamen; Free and Cued Selective Reminding Test and Rey-Osterrieth Complex Figure with thalamus, precuneus, and parahippocampal gyrus; Boston Naming Test with thalamus, caudate nucleus, and hippocampus; semantic verbal fluency with thalamus and phonological verbal fluency with caudate nucleus; and Tower of London test with frontal lobe, caudate nucleus, and posterior cingulate. Conclusion: Our study provides valuable data on the cortical and subcortical basis of cognitive function in MS. Neuropsychological tests mainly assessing attention and executive function showed a stronger association with caudate volume, while tests primarily evaluating memory were more strongly correlated with the thalamus. Other relevant regions were the posterior cingulate/precuneus, which were associated with attentional tasks, and several frontal regions, which were found to be correlated with planning and higher order executive functioning. Furthermore, our study supports the brain vertical organization of cognitive functioning, with the participation of the cortex, thalamus, basal ganglia, and cerebellum.
Human Factors in Data Science

Data Science (DS) has been shifting from libraries and stacks to usage and impact. While "database thinking" is permeating all levels in a DS stack, the DS lifecycle can only be fully realized by looping in humans in a principled and safe fashion. This paper focuses on the role of humans and user data in DS. It starts with the impact of human factors on the design of sustainable and fair data generation and curation. It then reviews how data processing and mining are being revisited to derive insights from user data. That is followed by how human-data interaction shapes the way we think about evaluating DS applications. The paper ends with opportunities that arise when bringing together database thinking and DS for humans.
6.5µW 92.3DB-DR Biopotential-Recording Front-End with 360MVPP Linear Input Range

This paper presents a direct-digital-conversion biopotential-recording front-end with 92.3dB dynamic range at an input range of $360\text{mV}_{\text{pp}}$. The proposed front-end is a capacitively - coupled 3rd-order Gm-C CT $\Delta\Sigma \text{M}$ with a 5-bit SAR quantizer. The capacitively-coupled summing circuit increases linearity of the $\Delta\Sigma \text{M}$, and a 3rd-order Gm-C loop filter and a 5-bit SAR ADC reduces quantization noise level. The proposed front-end is fabricated in $0.18\mu \text{m}$ CMOS, and achieves 91.3dB SNDR and $1.8\mu \text{V}_{\text{rms}}$ input-referred noise over 100Hz BW. Also, an in-vitro LFP recording is performed to verify the front-end.
Finite Element Methods for Multi-objective optimization of a High Step-up Interleaved Boost Converter

High step-up converters have been widely used in renewable energy systems and, recently, in automotive applications, due to their high voltage gain capability. Moreover, in these applications, efficiency and high-power density are usually required, although these characteristics are commonly opposite objectives. Therefore, a multi-objective optimization is quite useful in order to comply with both requirements of high efficiency and small size. In that sense, Finite Element Methods can be effective to complement optimization methods. This paper presents a procedure to optimize the efficiency and the volume of a high step-up converter that utilizes a coupled inductor with three windings installed in only one core. This optimization procedure is carried out using 3D and 2D Finite Element Method simulations. In this procedure, a complete modeling of power losses, size, and flux density is evaluated by comparing different materials and dimensions. The results of this modeling stage are introduced into a multi-objective optimization algorithm to obtain a Pareto front. Finally, the optimization methodology is validated by experimental tests.
Word Mover’s Embedding: From Word2Vec to Document Embedding

While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings. Recent work has demonstrated that a distance measure between documents called Word Mover’s Distance (WMD) that aligns semantically similar words, yields unprecedented KNN classification accuracy. However, WMD is expensive to compute, and it is hard to extend its use beyond a KNN classifier. In this paper, we propose the Word Mover’s Embedding (WME), a novel approach to building an unsupervised document (sentence) embedding from pre-trained word embeddings. In our experiments on 9 benchmark text classification datasets and 22 textual similarity tasks, the proposed technique consistently matches or outperforms state-of-the-art techniques, with significantly higher accuracy on problems of short length.