Reviewerly/exHarmony huggingface dataset:
An Recognition–Verification Mechanism for Real-Time Chinese Sign Language Recognition Based on Multi-Information Fusion

For online sign language recognition (SLR) based on inertial measurement unit (IMU) and a surface electromyography (sEMG) sensor, achieving high-accuracy is a major challenge. The traditional method for that is the segmentation–recognition mechanism, which has two key challenges: (1) it is difficult to design a highly robust segmentation method for online data with inconspicuous segmentation information; and (2) the diversity of input data will increase the burden of the classification. The recognition–verification mechanism was proposed to improve the performance of online SLR. In the recognition stage, we used sliding windows to pull the data, and applied a convolutional neural network (CNN) to classify the sign language signal. In the verification stage, the confidence was evaluated by the Siamese network to judge the correctness of the classification results. The accuracy and rapidity of the classification model were discussed for 86 categories of Chinese sign language. In the experiments for online SLR, the superiority of the recognition–verification mechanism compared to the segmentation–recognition mechanism was verified.
rORAM: Efficient Range ORAM with O(log2 N) Locality

Oblivious RAM protocols (ORAMs) allow a client to access data from an untrusted storage device without revealing to that device any information about their access pattern.Typically this is accomplished through random shuffling of the data such that the storage device cannot determine where individual blocks are located, resulting in a highly randomized access pattern.Storage devices however, are typically optimized for sequential access.A large number of random disk seeks during standard ORAM operation induce a substantial overhead.In this paper, we introduce rORAM, an ORAM specifically suited for accessing ranges of sequentially logical blocks while minimizing the number of random physical disk seeks.rORAM obtains significantly better asymptotic efficiency than prior designs (Asharov et al., ePrint 2017, Demertzis et al., CRYPTO 2018) reducing both the number of seeks and communication complexity by a multiplicative factor of O(log N ).An rORAM prototype is 30-50x times faster than Path ORAM for similar range-query workloads on local HDDs, 30x faster for local SSDs, and 10x faster for network block devices.rORAM's novel disk layout can also speed up standard ORAM constructions, e.g., resulting in a 2x faster Path ORAM variant.Importantly, experiments demonstrate suitability for real world applications -rORAM is up to 5x faster running a file server and up to 11x faster running a range-query intensive video server workloads compared to standard Path ORAM.
Optimizing a Verified SAT Solver

In previous work, I verified a SAT solver with dedicated imperative data structures, including the two-watched-literal scheme. In this paper, I extend this formalization with four additional optimizations. The approach is still based on refining an abstract calculus to a deterministic program. In turn, an imperative version is synthesized from the latter, which is then exported to Standard ML. The first optimization is the extension with blocking literals. Then, the memory management is improved in order to implement the heuristics necessary to implement search restart and forget, which were subsequently implemented. This required changes to the abstract calculus. Finally, the solver uses machine words until they overflow before switching to unbounded integers. Performance has improved and is now closer to MiniSAT without preprocessing.
Energy-Efficient Service Allocation Techniques in Cloud: A Survey

The demand for cloud computing infrastructure is increasing day by day to meet the requirement of small and medium enterprises. The data center-centric cloud technology has a high share of energy consumption from the IT-industry. The amount of energy consumption in a data center depends on the allocation of user service requests to virtual machines running on the different host. Minimization of energy consumption in the data center is a significant issue and addressed by optimal allocation of cloud resources. In this paper, we have discussed how service allocation strategies have been used to optimize the energy consumption in a cloud system. A generalized system architecture is presented based on which we define the service allocation problem and energy model. Further, we present the taxonomy of various energy-efficient resource allocation techniques found in the literature. In the end, various research challenges related to the energy-efficient service allocation in cloud are discussed.
Interpreting Black Box Models via Hypothesis Testing

In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments. In such high-stakes tasks, false discoveries may lead investigators astray. These applications would therefore benefit from control over the finite-sample error rate of interpretations. We reframe black box model interpretability as a multiple hypothesis testing problem. The task is to discover "important" features by testing whether the model prediction is significantly different from what would be expected if the features were replaced with uninformative counterfactuals. We propose two testing methods: one that provably controls the false discovery rate but which is not yet feasible for large-scale applications, and an approximate testing method which can be applied to real-world data sets. In simulation, both tests have high power relative to existing interpretability methods. When applied to state-of-the-art vision and language models, the framework selects features that intuitively explain model predictions. The resulting explanations have the additional advantage that they are themselves easy to interpret.
Electronic Identification for Universities: Building Cross-Border Services Based on the eIDAS Infrastructure

The European Union (EU) Regulation 910/2014 on electronic IDentification, Authentication, and trust Services (eIDAS) for electronic transactions in the internal market went into effect on 29 September 2018, meaning that EU Member States are required to recognize the electronic identities issued in the countries that have notified their eID schemes. Technically speaking, a unified interoperability platform—named eIDAS infrastructure—has been set up to connect the EU countries’ national eID schemes to allow a person to authenticate in their home EU country when getting access to services provided by an eIDAS-enabled Service Provider (SP) in another EU country. The eIDAS infrastructure allows the transfer of authentication requests and responses back and forth between its nodes, transporting basic attributes about a person, e.g., name, surname, date of birth, and a so-called eIDAS identifier. However, to build new eIDAS-enabled services in specific domains, additional attributes are needed. We describe our approach to retrieve and transport new attributes through the eIDAS infrastructure, and we detail their exploitation in a selected set of academic services. First, we describe the definition and the support for the additional attributes in the eIDAS nodes. We then present a solution for their retrieval from our university. Finally, we detail the design, implementation, and installation of two eIDAS-enabled academic services at our university: the eRegistration in the Erasmus student exchange program and the Login facility with national eIDs on the university portal.
On the computational power of RNNs

Recent neural network architectures such as the basic recurrent neural network (RNN) and Gated Recurrent Unit (GRU) have gained prominence as end-to-end learning architectures for natural language processing tasks. But what is the computational power of such systems? We prove that finite precision RNNs with one hidden layer and ReLU activation and finite precision GRUs are exactly as computationally powerful as deterministic finite automata. Allowing arbitrary precision, we prove that RNNs with one hidden layer and ReLU activation are at least as computationally powerful as pushdown automata. If we also allow infinite precision, infinite edge weights, and nonlinear output activation functions, we prove that GRUs are at least as computationally powerful as pushdown automata. All results are shown constructively.
ARHNet - Leveraging Community Interaction for Detection of Religious Hate Speech in Arabic

The rapid widespread of social media has led to some undesirable consequences like the rapid increase of hateful content and offensive language.Religious Hate Speech, in particular, often leads to unrest and sometimes aggravates to violence against people on the basis of their religious affiliations.The richness of the Arabic morphology and the limited available resources makes this task especially challenging.The current state-of-theart approaches to detect hate speech in Arabic rely entirely on textual (lexical and semantic) cues.Our proposed methodology contends that leveraging Community-Interaction can better help us profile hate speech content on social media.Our proposed ARHNet (Arabic Religious Hate Speech Net) model incorporates both Arabic Word Embeddings and Social Network Graphs for the detection of religious hate speech.
Stacked Self-Attention Networks for Visual Question Answering

Given a photograph, the task of Visual Question Answering (VQA) requires joint image and language understanding to answer a question. It is challenging in effectively extracting the visual representation of images, and efficiently embedding the textual sentences of questions. To address these challenges, we propose a VQA model that utilizes the stacked self-attention for visual understanding, and the BERT-based question embedding model. Particularly, the stacked self-attention mechanism proposed enables the model to not only focus on a simple object but also the relations between objects. Furthermore, the BERT model is learned in an end-to-end manner to better embed the question sentences. Our model is validated on the well-known VQA v2.0 dataset, and achieves the state-of-the-art results.
Designing Technology to Support Safety for Transgender Women &amp; Non-Binary People of Color

This work provides a preliminary understanding of how transgender women and non-binary people of color experience violence and manage safety, and what opportunities exist for HCI to support the safety needs of this community. We conducted nine interviews to understand how participants practice safety and what role technology played, if any, in these experiences. Interviewees expressed physical and psychological safety concerns, and managed safety by informing friends of their location using digital technologies, making compromises, and avoiding law enforcement. We designed U-Signal, a wearable technology and accompanying smartphone application prototype to increase physical safety and decrease safety concerns, reduce violence, and help build community.
Discovering Memory-Based Preferences for POI Recommendation in Location-Based Social Networks

Point-of-interest (POI) recommendations in location-based social networks (LBSNs) allow online users to discover various POIs for social activities occurring in the near future close to their current locations. Research has verified that people’s preferences regarding POIs are significantly affected by various internal and external contextual factors, which are therefore worth extensive study for POI recommendation. However, although psychological effects have also been demonstrated to be significantly correlated with an individual’s preferences, such effects have been largely ignored in previous studies on POI recommendation. For this paper, inspired by the famous memory theory in psychology, we were interested in whether memory-based preferences could be derived from users’ check-in data. Furthermore, we investigated how to incorporate these memory-based preferences into an effective POI recommendation scheme. Consequently, we refer to Ebbinghaus’s theory on memory, which describes the attenuation of an individual’s memory in the form of a forgetting curve over time. We first created a memory-based POI preference attenuation model and then adopted it to evaluate individuals’ check-ins. Next, we employed the memory-based values of check-ins to calculate the POI preference similarity between users in an LBSN. Finally, based on this memory-based preference similarity, we developed a novel POI recommendation method. We experimentally evaluated the proposed method on a real LBSN data set crawled from Foursquare. The results demonstrate that our method, which incorporates the proposed memory-based preference similarity for POI recommendation, significantly outperforms other methods. In addition, we found the best value of the parameter H in the memory-based preference model that optimizes the recommendation performance. This value of H implies that an individual’s memory usually has an effect on their daily travel choices for approximately 300 days.
Sensing is Believing

Biosensors-devices that sense the human body-are increasingly ubiquitous. However, it is unclear how people evaluate the risks associated with their use, in part because it is not well-understood what people believe these sensors can reveal. In this study, participants ranked biosensors by how likely they are to reveal what a person is thinking and feeling. We report quantitative and qualitative results of two survey-based studies, one on Mechanical Turk workers (n=100), and one on participants in a longitudinal self-tracking study (n=100). Our findings imply that, in the absence of information about particular sensing technologies, people rely on existing beliefs about the body to explain what they might reveal. Highlighting mismatches between perceived and actual technical capabilities, we contribute recommendations for designers and users.
Stability of Graph Scattering Transforms

Scattering transforms are non-trainable deep convolutional architectures that exploit the multi-scale resolution of a wavelet filter bank to obtain an appropriate representation of data. More importantly, they are proven invariant to translations, and stable to perturbations that are close to translations. This stability property dons the scattering transform with a robustness to small changes in the metric domain of the data. When considering network data, regular convolutions do not hold since the data domain presents an irregular structure given by the network topology. In this work, we extend scattering transforms to network data by using multiresolution graph wavelets, whose computation can be obtained by means of graph convolutions. Furthermore, we prove that the resulting graph scattering transforms are stable to metric perturbations of the underlying network. This renders graph scattering transforms robust to changes on the network topology, making it particularly useful for cases of transfer learning, topology estimation or time-varying graphs.
Memetic Particle Gravitation Optimization Algorithm for Solving Clustering Problems

Data clustering is a well-known data analysis technique for organizing unlabeled datapoints into clusters on the basis of similarity measures. The real-world applications of data clustering include bioinformatics, vector quantization, data mining, geographical information systems, pattern recognition, image processing, and wireless sensors. The data in a cluster are similar (minimizing the intra-cluster distance) and differ from the data in other clusters (maximizing the inter-cluster distance). The cluster problem has been proven to be NP-hard, but can be solved using meta-heuristic algorithms, such as ant colony optimization, genetic algorithms, gravitational search algorithm (GSA), and particle swarm optimization (PSO). This paper proposes a memetic clustering algorithm with efficient search and fast convergence, respectively, based on PSO and GSA, called the memetic particle gravitation optimization (MPGO) algorithm. The two main mechanisms of MPGO are hybrid operation and diversity enhancement. The former involves the exchange of individuals from two subpopulations after a predefined number of function evaluations (FEs), whereas the latter involves an enhancement operator, which is similar to the crossover process of differential evolution, for enhancing the diversity of each system. Individuals from the PSO and GSA systems are selected for the exchange of solutions by using the roulette-wheel approach. The performance of the proposed algorithm was evaluated on 52 benchmark test functions, six UCI machine learning benchmarks, and image segmentation of six well-known images. A comparison with existing algorithms verified the superior performance of the proposed algorithm in terms of a fitness value, an accuracy rate, and a peak signal-to-noise ratio.
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation

We present a neural approach called IRNet for complex and cross-domain Text-to-SQL. IRNet aims to address two challenges: 1) the mismatch between intents expressed in natural language (NL) and the implementation details in SQL; 2) the challenge in predicting columns caused by the large number of out-of-domain words. Instead of end-to-end synthesizing a SQL query, IRNet decomposes the synthesis process into three phases. In the first phase, IRNet performs a schema linking over a question and a database schema. Then, IRNet adopts a grammar-based neural model to synthesize a SemQL query which is an intermediate representation that we design to bridge NL and SQL. Finally, IRNet deterministically infers a SQL query from the synthesized SemQL query with domain knowledge. On the challenging Text-to-SQL benchmark Spider, IRNet achieves 46.7% accuracy, obtaining 19.5% absolute improvement over previous state-of-the-art approaches. At the time of writing, IRNet achieves the first position on the Spider leaderboard.
Strategies for Structuring Story Generation

Writers generally rely on plans or sketches to write long stories, but most current language models generate word by word from left to right. We explore coarse-to-fine models for creating narrative texts of several hundred words, and introduce new models which decompose stories by abstracting over actions and entities. The model first generates the predicate-argument structure of the text, where different mentions of the same entity are marked with placeholder tokens. It then generates a surface realization of the predicate-argument structure, and finally replaces the entity placeholders with context-sensitive names and references. Human judges prefer the stories from our models to a wide range of previous approaches to hierarchical text generation. Extensive analysis shows that our methods can help improve the diversity and coherence of events and entities in generated stories.
Deep-Learning-Based Semantic Labeling for 2D Mammography and Comparison of Complexity for Machine Learning Tasks

Machine learning has several potential uses in medical imaging for semantic labeling of images to improve radiologist workflow and to triage studies for review. The purpose of this study was to (1) develop deep convolutional neural networks (DCNNs) for automated classification of 2D mammography views, determination of breast laterality, and assessment and of breast tissue density; and (2) compare the performance of DCNNs on these tasks of varying complexity to each other. We obtained 3034 2D-mammographic images from the Digital Database for Screening Mammography, annotated with mammographic view, image laterality, and breast tissue density. These images were used to train a DCNN to classify images for these three tasks. The DCNN trained to classify mammographic view achieved receiver-operating-characteristic (ROC) area under the curve (AUC) of 1. The DCNN trained to classify breast image laterality initially misclassified right and left breasts (AUC 0.75); however, after discontinuing horizontal flips during data augmentation, AUC improved to 0.93 (p < 0.0001). Breast density classification proved more difficult, with the DCNN achieving 68% accuracy. Automated semantic labeling of 2D mammography is feasible using DCNNs and can be performed with small datasets. However, automated classification of differences in breast density is more difficult, likely requiring larger datasets.
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy

We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available.Furthermore, the test tag-set is not identical to any individual training tag-set.Yet, the relations between all tags are provided in a tag hierarchy, covering the test tags as a combination of training tags.This setting occurs when various datasets are created using different annotation schemes.This is also the case of extending a tag-set with a new tag by annotating only the new tag in a new dataset.We propose to use the given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets.We compare this model to combining independent models and to a model based on the multitasking approach.Our experiments show the benefit of the tag-hierarchy model, especially when facing non-trivial consolidation of tag-sets.
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent

Despite its empirical success and recent theoretical progress, there generally lacks a quantitative analysis of the effect of batch normalization (BN) on the convergence and stability of gradient descent. In this paper, we provide such an analysis on the simple problem of ordinary least squares (OLS). Since precise dynamical properties of gradient descent (GD) is completely known for the OLS problem, it allows us to isolate and compare the additional effects of BN. More precisely, we show that unlike GD, gradient descent with BN (BNGD) converges for arbitrary learning rates for the weights, and the convergence remains linear under mild conditions. Moreover, we quantify two different sources of acceleration of BNGD over GD -- one due to over-parameterization which improves the effective condition number and another due having a large range of learning rates giving rise to fast descent. These phenomena set BNGD apart from GD and could account for much of its robustness properties. These findings are confirmed quantitatively by numerical experiments, which further show that many of the uncovered properties of BNGD in OLS are also observed qualitatively in more complex supervised learning problems.
Phosphenes

Hybrid practices are emerging that integrate creative materials like paint, clay, and cloth with intangible immaterials like computation, electricity, and heat. This work aims to expand the design potential of immaterial elements by transforming them into manipulatable, observable and intuitive materials. We explore one such immaterial, electric heat, and develop a maker-friendly fabrication pipeline and crafting support tool that allows users to experientially compose resistive heaters that generate heat spatially and temporally. These heaters are then used to couple heat and thermoreactive materials in a class of artifacts we term Thermoreactive Composites (TrCs). In a formal user study, we observe how designing fabrication workflows along dimensions of composability and perceivability better matches the working styles of material practitioners without domain knowledge of electronics. Through exemplar artifacts, we demonstrate the potential of heat as a creative material and discuss implications for immaterials used within creative practices.