Reviewerly/exHarmony huggingface dataset:
Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms

Mixture-of-Experts (MoE) is a widely popular model for ensemble learning and is a basic building block of highly successful modern neural networks as well as a component in Gated Recurrent Units (GRU) and Attention networks. However, present algorithms for learning MoE, including the EM algorithm and gradient descent, are known to get stuck in local optima. From a theoretical viewpoint, finding an efficient and provably consistent algorithm to learn the parameters remains a long standing open problem for more than two decades. In this paper, we introduce the first algorithm that learns the true parameters of a MoE model for a wide class of non-linearities with global consistency guarantees. While existing algorithms jointly or iteratively estimate the expert parameters and the gating parameters in the MoE, we propose a novel algorithm that breaks the deadlock and can directly estimate the expert parameters by sensing its echo in a carefully designed cross-moment tensor between the inputs and the output. Once the experts are known, the recovery of gating parameters still requires an EM algorithm; however, we show that the EM algorithm for this simplified problem, unlike the joint EM algorithm, converges to the true parameters. We empirically validate our algorithm on both the synthetic and real data sets in a variety of settings, and show superior performance to standard baselines.
Use Your Head! Exploring Interaction Modalities for Hat Technologies

As our landscape of wearable technologies proliferates, we find more devices situated on our heads. However, many challenges hinder them from widespread adoption - from their awkward, bulky form factor (today's AR and VR goggles) to their socially stigmatized designs (Google Glass) and a lack of a well-developed head-based interaction design language. In this paper, we explore a socially acceptable, large, head-worn interactive wearable - a hat. We report results from a gesture elicitation study with 17 participants, extract a taxonomy of gestures, and define a set of design concerns for interactive hats. Through this lens, we detail the design and fabrication of three hat prototypes capable of sensing touch, head movements, and gestures, and including ambient displays of several types. Finally, we report an evaluation of our hat prototype and insights to inform the design of future hat technologies.
Leaning into Browser Extensions

An array of browser extensions has been developed to help researchers locate full-text content, whether that full text is available in an institutional repository, on a publishers site, or in an open access journal. Examples of such browser extensions include Unpaywall, Kopernio, EZproxy tools, and Lean Library. This Balance Point column focuses on the growth of browser extensions, the need they are filling, and their adoption by libraries. Particular attention is paid to the Lean Library browser extension and the services offered by Lean Library. Drawing on the literature and interviews with librarians in the field, the advantages and disadvantages of browser extensions are addressed, as well as security concerns.
<i>Retracted on May 10, 2023:</i> TPShare: a time-space sharing scheduling abstraction for shared cloud via vertical labels

Current shared cloud operating systems (cloud OS) such as Mesos and YARN are based on the "de facto" two-horizontal-layer cloud platform architecture, which decouples cloud application frameworks (e.g., Apache Spark) from the underlying resource management infrastructure. Each layer normally has its own task or resource allocation scheduler, based on either time-sharing or space-sharing. As such, the schedulers in different layers are unavoidably disconnected, --- not aware of each other, which highly likely leads to resource (e.g.,CPU) wastes. Moreover, the tail latency may even be harmed due to the performance interference on shared resources.
Training CNNs with Selective Allocation of Channels

Recent progress in deep convolutional neural networks (CNNs) have enabled a simple paradigm of architecture design: larger models typically achieve better accuracy. Due to this, in modern CNN architectures, it becomes more important to design models that generalize well under certain resource constraints, e.g. the number of parameters. In this paper, we propose a simple way to improve the capacity of any CNN model having large-scale features, without adding more parameters. In particular, we modify a standard convolutional layer to have a new functionality of channel-selectivity, so that the layer is trained to select important channels to re-distribute their parameters. Our experimental results under various CNN architectures and datasets demonstrate that the proposed new convolutional layer allows new optima that generalize better via efficient resource utilization, compared to the baseline.
R2P2: making RPCs first-class datacenter citizens

Remote Procedure Calls are widely used to connect data-center applications with strict tail-latency service level objectives in the scale of µs. Existing solutions utilize streaming or datagram-based transport protocols for RPCs that impose overheads and limit the design flexibility. Our work exposes the RPC abstraction to the endpoints and the network, making RPCs first-class datacenter citizens and allowing for in-network RPC scheduling.We propose R2P2, a UDP-based transport protocol specifically designed for RPCs inside a datacenter. R2P2 exposes pairs of requests and responses and allows efficient and scalable RPC routing by separating the RPC target selection from request and reply streaming. Leveraging R2P2, we implement a novel join-bounded-shortest-queue (JBSQ) RPC load balancing policy, which lowers tail latency by centralizing pending RPCs in the router and ensures that requests are only routed to servers with a bounded number of outstanding requests. The R2P2 router logic can be implemented either in a software middlebox or within a P4 switch ASIC pipeline.Our evaluation, using a range of microbenchmarks, shows that the protocol is suitable for µs-scale RPCs and that its tail latency outperforms both random selection and classic HTTP reverse proxies. The P4-based implementation of R2P2 on a Tofino ASIC adds less than 1µs of latency whereas the software middlebox implementation adds 5µs latency and requires only two CPU cores to route RPCs at 10 Gbps linerate. R2P2 improves the tail latency of web index searching on a cluster of 16 workers operating at 50% of capacity by 5.7× over NGINX. R2P2 improves the throughput of the Redis key-value store on a 4-node cluster with master/slave replication for a tail-latency service-level objective of 200µs by more than 4.8× vs. vanilla Redis.
Designing a robotic smart home for everyone, especially the elderly and people with disabilities.

We initiated the Robotic Smart Home (RSH) project to develop a comfortable, safe home environment for all people, including the elderly and individuals with disabilities. An important consideration when introducing robots into a home environment is the confined living space, the so-called space problem. The RSH project plans to simultaneously develop robots and an architectural design for living spaces to create an optimal home environment that will help elderly people live independently at home for longer periods. The RSH accommodates the following three robotics and assistive systems: mobility and transfer assist system, operational assist system, and information assist system. The mobility and transfer assist system includes three types of devices (lifting type, lateral-transfer type, and suspension type), which can be available to users as appropriate according to the severity of their disability. The operational assist system combines a hand robot with an environmental control system for the convenience of users. An information assist system connects the RSH with remote locations for communication. Inside the RSH, a home automation and monitoring system connected to the Internet of Things provides residents with comfort and security. As part of this project, two RSH centers have been established for effective facility adoption.
A Context-based Framework for Resource Citation Classification in Scientific Literatures

In this paper, we introduce the task of resource citation classification for scientific literature using a context-based framework. This task is to analyze the purpose of citing an on-line resource in scientific text by modeling the role and function of each resource citation. It can be incorporated into resource indexing and recommendation systems to help better understand and classify on-line resources in scientific literature. We propose a new annotation scheme for this task and develop a dataset of 3,088 manually annotated resource citations. We adopt a neural-based model to build the classifiers and apply them on the large ARC dataset to examine the revolution of scientific resources from trends in their function over time.
Integration of Relational and NoSQL Databases

The analysis of relational and NoSQL databases leads to the conclusion that these data processing systems are to some extent complementary. In the current Big Data applications, especially where extensive analyses (so-called Big Analytics) are needed, it turns out that it is nontrivial to design an infrastructure involving data and software of both types. Unfortunately, the complementarity negatively influences integration possibilities of these data stores both at the data model and data processing levels. In terms of performance, it may be beneficial to use a polyglot persistence, a multimodel approach or multilevel modeling, or even to transform the SQL database schema into NoSQL and to perform data migration between the relational and NoSQL databases. Another possibility is to integrate a NoSQL database and relational database with the help of a third data model. The aim of the paper is to show these possibilities and present some new methods of designing such integrated database architectures.
Bayesian Inference Semantics: A Modelling System and A Test Suite

We present BIS, a Bayesian Inference Semantics, for probabilistic reasoning in natural language.The current system is based on the framework of Bernardy et al. ( 2018), but departs from it in important respects.BIS makes use of Bayesian learning for inferring a hypothesis from premises.This involves estimating the probability of the hypothesis, given the data supplied by the premises of an argument.It uses a syntactic parser to generate typed syntactic structures that serve as input to a model generation system.Sentences are interpreted compositionally to probabilistic programs, and the corresponding truth values are estimated using sampling methods.BIS successfully deals with various probabilistic semantic phenomena, including frequency adverbs, generalised quantifiers, generics, and vague predicates.It performs well on a number of interesting probabilistic reasoning tasks.It also sustains most classically valid inferences (instantiation, de Morgan's laws, etc.).To test BIS we have built an experimental test suite with examples of a range of probabilistic and classical inference patterns.
Hamming Distance Completeness

We show, given a binary integer function diamond that is piecewise polynomial, that (+,diamond) vector products are equivalent under one-to-polylog reductions to the computation of the Hamming distance. Examples include the dominance and l_{2p+1} distances for constant p. Our results imply equivalence (up to polylog factors) between the complexity of computing All Pairs Hamming Distance, All Pairs l_{2p+1} Distance and Dominance Matrix Product, and equivalence between Hamming Distance Pattern Matching, l_{2p+1} Pattern Matching and Less-Than Pattern Matching. The resulting algorithms for l_{2p+1} Pattern Matching and All Pairs l_{2p+1}, for 2p+1 = 3,5,7,... are likely to be optimal, given lack of progress in improving upper bounds for Hamming distance in the past 30 years. While reductions between selected pairs of products were presented in the past, our work is the first to generalize them to a general class of functions, showing that a wide class of intermediate complexity problems are in fact equivalent.
Time-Aware and Location-Based Personalized Collaborative Recommendation for IoT Services

IoT applications need to actively monitor and respond to service invokes to guarantee the reliable connectivity of data and devices. However, with the gradual increasing of dataset, it is difficult to provide accurate and effective service in time. In order to solve the problem of information overload, the recommendation system has been proposed. In recent years, there are some progresses in the research of recommendation system based on collaborative filtering (CF), but most of them face sparse problems and scalable problems. In this paper, a personalized recommendation model is given building on location and time information. Data sparsity is alleviated by padding the missing value of the user-service-time tensor over the adjacent time period. Users set and services set are divided into multiple clusters respectively based on location information and similar items are selected in smaller and highly similar clusters, which makes our model scalable. The time decay function and the personalized weight is exploited in our method to improve the prediction accuracy. Massive experiments based on real-world data indicate that our method can effectively improve the accuracy of prediction compared to other models.
Reading Behavior and Comprehension of C++ Source Code - A Classroom Study

This paper presents an eye-tracking study conducted in a classroom setting with seventeen students enrolled in a Computer Science program. The students were a mix of twelve first-year undergraduates (novices) and five masters students (non-novices). Students were asked to answer a comprehension question for each of thirteen C++ programs after reading them. Each program is split into a series of chunks which logically break down meaningful parts where eye gazes hint at cognition about parts of programs useful to solving problems. We analyze these patterns across chunks for the stories they tell about how participants went about searching for cues, and to learn whether their gaze patterns predicted accurate answers to three types of questions The results show that novices tend to visit print output statements and declaration statements the same amount as they do other statements in code with the exception of control block headers, which both groups tend to focus on the most across all categories. We also find that non-novices spend longer fixating inside of chunks of code before transitioning to other chunks, and tend to transition to chunks that are further away from their original position than novices.
Multi-objective collective search and movement-based metrics in swarm robotics

Particle Swarm Optimization is a well researched meta-heuristic for single- and multi-objective problems. It is based on the movement of particles, which enables its application to collective search in robotic applications. However, in the robotic context some assumptions regarding performance measurement of PSO-algorithms do not apply. Concerning energy and time, while reading a sensor is usually cheap, the movement is costly in robotic applications. Traditional methods do not take into account any cost metrics associated with the actual movement of the particles. Therefore, new metrics are required to understand how well a PSO-algorithm can perform as a collective search mechanism. This article proposes two metrics, the Normalized Movement Energy Cost and Normalized Movement Time Cost that enable researchers to analyze an algorithm's performance not just regarding the obtained solution quality, but also with respect to movement time and energy costs.
Policy Creation for Enterprise-Level Data Sharing

Enterprises, including military, law enforcement, medical, financial, and commercial organizations, must often share large quantities of data, some potentially sensitive, with many other enterprises. A key issue, the mechanics of data sharing, involves how to precisely and unambiguously specify which data to share with which partner or group of partners. This issue can be addressed through a system of formal data sharing policy definitions and automated enforcement. Several challenges arise when specifying enterprise-level data sharing policies. A first challenge involves the scale and complexity of data types to be shared. An easily understood method is required to represent and visualize an enterprise’s data types and their relationships so that users can quickly, easily, and precisely specify which data types and relationships to share. A second challenge involves the scale and complexity of data sharing partners. Enterprises typically have many partners involved in different projects, and there are often complex hierarchies among groups of partners that must be considered and navigated to specify which partners or groups of partners to include in a data sharing policy. A third challenge is that defining policies formally, given the first two challenges of scale and complexity, requires complex, precise language, but these languages are difficult to use by non-specialists. More useable methods of policy specification are needed. Our approach was to develop a software wizard that walks users through a series of steps for defining a data sharing policy. A combination of innovative and well known methods is used to address these challenges of scale, complexity, and usability.
Detecting spelling errors in compound and pseudocompound words.

Three experiments using a spelling error detection task investigated the extent to which morphemes and pseudomorphemes affect word processing. We compared the processing of transparent compound words (e.g., doorbell), pseudocompound words (e.g., carpet), and matched control words (e.g., tomato). In half of the compound and pseudocompound words, spelling errors were created by transposing adjacent letters and in half of the control words, errors were created by transposing letters at the same location as the matched compound or pseudocompound words. Correctly spelled compound words were more easily processed than matched control words, but this advantage was removed when letter transpositions were introduced at the morpheme boundary. In contrast, misspelled pseudocompound words showed a processing deficit relative to their matched control words when letter transpositions were introduced at the (pseudo)morpheme boundary. The results strongly suggest that morphological processing is attempted obligatorily when the orthography indicates that morphological structure is present. However, the outcomes of the morphological processing attempts are different for compounds and pseudocompounds, as might be expected, given that only the compounds have a morphological structure that matches the structure suggested by the orthography. The findings reflect 2 effects: an orthographic effect that is facilitatory and not sensitive to morphological structure of the whole word, and a morphemic effect that is facilitatory for compounds but inhibitory for pseudocompounds. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
Fast Object Class Labelling via Speech

Object class labelling is the task of annotating images with labels on the presence or absence of objects from a given class vocabulary. Simply asking one yes-no question per class, however, has a cost that is linear in the vocabulary size and is thus inefficient for large vocabularies. Modern approaches rely on a hierarchical organization of the vocabulary to reduce annotation time, but remain expensive (several minutes per image for the 200 classes in ILSVRC). Instead, we propose a new interface where classes are annotated via speech. Speaking is fast and allows for direct access to the class name, without searching through a list or hierarchy. As additional advantages, annotators can simultaneously speak and scan the image for objects, the interface can be kept extremely simple, and using it requires less mouse movement. As annotators using our interface should only say words from a given class vocabulary, we propose a dedicated task to train them to do so. Through experiments on COCO and ILSVRC, we show our method yields high-quality annotations at 2.3x −14.9x less annotation time than existing methods.
Optimal rank and select queries on dictionary-compressed text

We study the problem of supporting queries on a string $S$ of length $n$ within a space bounded by the size $\gamma$ of a string attractor for $S$. Recent works showed that random access on $S$ can be supported in optimal $O(\log(n/\gamma)/\log\log n)$ time within $O\left (\gamma \rm{polylog} n \right)$ space. In this paper, we extend this result to \emph{rank} and \emph{select} queries and provide lower bounds matching our upper bounds on alphabets of polylogarithmic size. Our solutions are given in the form of a space-time trade-off that is more general than the one previously known for grammars and that improves existing bounds on LZ77-compressed text by a $\log\log n$ time-factor in \emph{select} queries. We also provide matching lower and upper bounds for \emph{partial sum} and \emph{predecessor} queries within attractor-bounded space, and extend our lower bounds to encompass navigation of dictionary-compressed tree representations.
Aggregation of Time-Series Data Under Differential Privacy

In this work, we investigate the problem of statistical data analysis while preserving user privacy in the distributed and semi-honest setting. Particularly, we study properties of Private Stream Aggregation (PSA) schemes, first introduced by Shi et al. in 2011. A PSA scheme is a secure multiparty protocol for the aggregation of time-series data in a distributed network with a minimal communication cost. We show that in the non-adaptive query model, secure PSA schemes can be built upon any key-homomorphic weak pseudo-random function (PRF) and we provide a tighter security reduction. In contrast to the aforementioned work, this means that our security definition can be achieved in the standard model. In addition, we give two computationally efficient instantiations of this theoretic result. The security of the first instantiation comes from a key-homomorphic weak PRF based on the Decisional Diffie-Hellman problem and the security of the second one comes from a weak PRF based on the Decisional Learning with Errors problem. Moreover, due to the use of discrete Gaussian noise, the second construction inherently maintains a mechanism that preserves $$(\epsilon ,\delta )$$ -differential privacy in the final data-aggregate. A consequent feature of the constructed protocol is the use of the same noise for security and for differential privacy. As a result, we obtain an efficient prospective post-quantum PSA scheme for differentially private data analysis in the distributed model.
Interdependent Routines and Innovation Processes – An Ethnographic Study of Scrum Teams

Organizational routines are an important concept to understand how organizations coordinate their work. Most research on organizational routines seems to assume that a concise description of the outcomes that routines produce exists a priori. Work in organizations, however, is oftentimes characterized by high degrees of complexity, uncertainty and ambiguity. In such cases, the outcomes that routines produce are only partially defined in advance and constantly change their shape over time. Hence, this paper focuses on contexts, like innovation processes, in which routines produce outcomes that are largely unknown in the beginning, but are constantly in the process of becoming. We combine theory on epistemic objects and routines to understand how software development teams of a medium sized company deal with highly complex and ambiguous issues to be resolved. We present our ethnographic study of these teams working with the Scrum framework and show that performing multiple organizational routines orchestrates the unfolding of innovative processes.