Reviewerly/exHarmony huggingface dataset:
Cerebro

Artificial Neural Networks (ANNs) are revolutionizing many machine learning (ML) applications. But there is a major bottleneck to wider adoption: the pain of model selection. This empirical process involves exploring the ANN architecture and hyper-parameters, often requiring hundreds of trials. Alas, most ML systems focus on training one model at a time, reducing throughput and raising costs; some also sacrifice reproducibility. We present our vision of Cerebro, a system to raise ANN model selection throughput at scale and ensure reproducibility. Cerebro uses a novel parallel execution strategy we call model hopper parallelism. We discuss the research questions in building Cerebro and present promising initial empirical results.
Quick Leukocyte Nucleus Segmentation in Leukocyte Counting

The leukocyte nucleus quick segmentation is one of the key techniques in leukocyte real-time online scanning of human blood smear. We propose a quick leukocyte nucleus segmentation method based on the component difference in RGB color space. By analyzing the captured microscopic images of the peripheral blood smears from the autoscanning microscope, it is found that the difference values between B component and G component (B - G values) in the regions of the leukocyte nuclei and the platelets are much bigger than those in the other regions, even in the regions including the stains. So, the B - G values can segment the leukocyte nuclei and the platelets with an appropriate empirical threshold because the platelets are much smaller than the leukocyte nuclei, so the leukocyte nuclei can be segmented by size filtering. Also, only an 8 bit subtraction operation is performed for the B - G values, and it can improve the leukocyte nucleus segmentation speed significantly. Experimental results show that the proposed method performs well for the five types of leukocyte segmentation with a quick speed. It is very suitable for the real-time peripheral blood smear autoscanning test application. In addition, the five types of leukocytes can be counted accurately.
Applying knowledge bases to make factories smarter

Abstract Knowledge Bases (KBs) enable engineers to capture knowledge in a formalized way. This formalization allows us to combine knowledge, thus creating the basis for smart factories while also supporting product and production system design. Building comprehensive and reusable KBs is still a challenge, though, especially for knowledge-intensive domains like engineering and production. To cope with the sheer amount of knowledge, engineers should reuse existing KBs. This paper presents a comprehensive overview of domain-specific KBs for production and engineering, as well as generic top-level ontologies. The application of such top-level ontologies offers new insights by integrating knowledge from various domains, stakeholders, and companies. To bridge the gap between top-level ontologies and existing domain KBs, we introduce an Intermediate Engineering Ontology (IEO).
SUMBT: Slot-Utterance Matching for Universal and Scalable Belief Tracking

In goal-oriented dialog systems, belief trackers estimate the probability distribution of slot-values at every dialog turn. Previous neural approaches have modeled domain- and slot-dependent belief trackers, and have difficulty in adding new slot-values, resulting in lack of flexibility of domain ontology configurations. In this paper, we propose a new approach to universal and scalable belief tracker, called slot-utterance matching belief tracker (SUMBT). The model learns the relations between domain-slot-types and slot-values appearing in utterances through attention mechanisms based on contextual semantic vectors. Furthermore, the model predicts slot-value labels in a non-parametric way. From our experiments on two dialog corpora, WOZ 2.0 and MultiWOZ, the proposed model showed performance improvement in comparison with slot-dependent methods and achieved the state-of-the-art joint accuracy.
SCU

Graph processing algorithms are key in many emerging applications in areas such as machine learning and data analytics. Although the processing of large scale graphs exhibits a high degree of parallelism, the memory access pattern tend to be highly irregular, leading to poor GPGPU efficiency due to memory divergence. To ameliorate this issue, GPGPU applications perform a stream compaction operation each iteration of the algorithm to extract the subset of active nodes/edges, so subsequent steps work on compacted dataset.
Fast constant-time gcd computation and modular inversion

This paper introduces streamlined constant-time variants of Euclid’s algorithm, both for polynomial inputs and for integer inputs. As concrete applications, this paper saves time in (1) modular inversion for Curve25519, which was previously believed to be handled much more efficiently by Fermat’s method, and (2) key generation for the ntruhrss701 and sntrup4591761 lattice-based cryptosystems.
Researching public library programs through Facebook events: a new research approach

Purpose The purpose of this paper is to report on a new approach for researching public library programs through Facebook events. The term public library programs refers to publicly announced activities and events taking place within or in relation to a public library. In Denmark, programs are an important part of the practices of public libraries and have been growing in both number and variety within recent years. Design/methodology/approach The data for the study presented in this paper consists of Facebook events announcing public library programs. In the study of this data, grounded theory is used as a research strategy and methods of web archiving are used for collecting both the textual and the visual content of the Facebook events. Findings The combination of Facebook events as data, grounded theory as a research strategy and web archiving as methods for data collection proves to be useful for researching the format and content of public library programs, which have already taken place. Research limitations/implications Only a limited number of Facebook events are examined and the context is restricted to one country. Originality/value This paper presents a promising approach for researching public library programs through social media content and provides new insights into both methods and data as well as the phenomenon investigated. Thereby, this paper contributes to a conception of an under-developed researched area as well as a new approach for studying it.
Identifying Emotions from Walking using Affective and Deep Features

We present a new data-driven model and algorithm to identify the perceived emotions of individuals based on their walking styles. Given an RGB video of an individual walking, we extract his/her walking gait in the form of a series of 3D poses. Our goal is to exploit the gait features to classify the emotional state of the human into one of four emotions: happy, sad, angry, or neutral. Our perceived emotion recognition approach uses deep features learned via LSTM on labeled emotion datasets. Furthermore, we combine these features with affective features computed from gaits using posture and movement cues. These features are classified using a Random Forest Classifier. We show that our mapping between the combined feature space and the perceived emotional state provides 80.07% accuracy in identifying the perceived emotions. In addition to classifying discrete categories of emotions, our algorithm also predicts the values of perceived valence and arousal from gaits. We also present an EWalk (Emotion Walk) dataset that consists of videos of walking individuals with gaits and labeled emotions. To the best of our knowledge, this is the first gait-based model to identify perceived emotions from videos of walking individuals.
Self-Organizing Migrating Algorithm Pareto

In this paper, we propose a new method named Pareto-based self-organizing migrating algorithm (SOMA Pareto), in which the algorithm is divided into the Organization, Migration, and Update processes. The important key in the Organization process is the application of the Pareto Principle to select the Migrant and the Leader, increasing the performance of the algorithm. The adaptive PRT, Step, and PRTVector parameters are applied to enhance the ability to search for promising subspaces and then to focus on exploiting that subspaces. Based on the testing results on the well-known benchmark suites such as CEC'13, CEC'15, and CEC'17, the superior performance of the proposed algorithm compared to the SOMA family and the state-of-the-art algorithms such as variant DE and PSO are confirmed. These results demonstrate that SOMA Pareto is an effective, promising algorithm.
We don’t know we don’t know: asserting ignorance

The pragmatic logic of assertions shows a connection between ignorance and (informal) decidability. In it, we can express pragmatic factual ignorance and first-order ignorance as well as some of their variants. We also show how some pragmatic versions of second-order ignorance and of Rumsfeld-ignorance may be formulated. A specific variant of second-order ignorance is particularly relevant. This indicates a strong pragmatic version of ignorance of ignorance, irreducible to any previous form of ignorance, which defines limits to what can justifiably be asserted about higher-order ignorance. Finally, we relate the justified assertion of second-order ignorance (that cannot be known) with scientific assertions.
Evaluating Language Model Finetuning Techniques for Low-resource Languages

Unlike mainstream languages (such as English and French), low-resource languages often suffer from a lack of expert-annotated corpora and benchmark resources that make it hard to apply state-of-the-art techniques directly. In this paper, we alleviate this scarcity problem for the low-resourced Filipino language in two ways. First, we introduce a new benchmark language modeling dataset in Filipino which we call WikiText-TL-39. Second, we show that language model finetuning techniques such as BERT and ULMFiT can be used to consistently train robust classifiers in low-resource settings, experiencing at most a 0.0782 increase in validation error when the number of training examples is decreased from 10K to 1K while finetuning using a privately-held sentiment dataset.
Indoor Image Representation by High-Level Semantic Features

Indoor image features extraction is a fundamental problem in multiple fields such as image processing, pattern recognition, robotics and so on. Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e.g., object association). These techniques, therefore, involve undesired classification performance. To tackle this issue, we propose the notion of high-level semantic features and design four steps to extract them. Specifically, we first construct the objects pattern dictionary through extracting raw objects in the images, and then retrieve and extract semantic objects from the objects pattern dictionary. We finally extract our high-level semantic features based on the calculated probability and delta parameter. Experiments on three publicly available datasets (MIT-67, Scene15 and NYU V1) show that our feature extraction approach outperforms state-of-the-art feature extraction methods for indoor image classification, given a lower dimension of our features than those methods.
Experimental Measurement of the Hilbert-Schmidt Distance between Two-Qubit States as a Means for Reducing the Complexity of Machine Learning

We report on the experimental measurement of the Hilbert-Schmidt distance between two two-qubit states by many-particle interference. We demonstrate that our three-step method for measuring distances in the Hilbert space is far less complex than reconstructing density matrices and that it can be applied in quantum-enhanced machine learning to reduce the complexity of calculating Euclidean distances between multidimensional points, which can be especially interesting for near term quantum technologies and quantum artificial intelligence research. Our results are also a novel example of applying mixed states in quantum information processing. Usually working with mixed states is undesired, but here it gives the possibility of encoding extra information as the degree of coherence between the given two dimensions of the density matrix.
Geochemical and Geostatistical Studies for Estimating Gold Grade in Tarq Prospect Area by K-Means Clustering Method

Tarq geochemical 1:100,000 Sheet is located in Isfahan province which is investigated by Iran's Geological and Explorations Organization using stream sediment analyzes.This area has stratigraphy of Precambrian to Quaternary rocks and is located in the Central Iran zone.According to the presence of signs of gold mineralization in this area, it is necessary to identify important mineral areas in this area.Therefore, finding information is necessary about the relationship and monitoring the elements of gold, arsenic, and antimony relative to each other in this area to determine the extent of geochemical halos and to estimate the grade.Therefore, a well-known and useful K-means method is used for monitoring the elements in the present study, this is a clustering method based on minimizing the total Euclidean distances of each sample from the center of the classes which are assigned to them.In this research, the clustering quality function and the utility rate of the sample have been used in the desired cluster (S(i)) to determine the optimum number of clusters.Finally, with regard to the cluster centers and the results, the equations were used to predict the amount of the gold element based on four parameters of arsenic and antimony grade, length and width of sampling points.
LSTM Language Models for LVCSR in First-Pass Decoding and Lattice-Rescoring

LSTM based language models are an important part of modern LVCSR systems as they significantly improve performance over traditional backoff language models. Incorporating them efficiently into decoding has been notoriously difficult. In this paper we present an approach based on a combination of one-pass decoding and lattice rescoring. We perform decoding with the LSTM-LM in the first pass but recombine hypothesis that share the last two words, afterwards we rescore the resulting lattice. We run our systems on GPGPU equipped machines and are able to produce competitive results on the Hub5'00 and Librispeech evaluation corpora with a runtime better than real-time. In addition we shortly investigate the possibility to carry out the full sum over all state-sequences belonging to a given word-hypothesis during decoding without recombination.
On the day-of-the-week effects of Bitcoin markets: international evidence

Purpose The purpose of this paper is to examine the day-of-the-week effects of Bitcoin (BTC) markets on the exchange level from January 2014 to September 2018. Design/methodology/approach The in-depth study on the day-of-the-week effects is conducted by using data consisting of Bitcoin prices denominated in 20 fiat currencies from 23 Bitcoin trading exchanges through the method of rolling sample for calendar effect proposed by Zhang et al. (2017). Findings It is shown by the empirical results that different patterns of the day-of-the-week effects are observed on Bitcoin denominated in various fiat currencies by referring to the price data collected from exchanges. Furthermore, the patterns of the day-of-the-week effects are also available after adjusting Bitcoin prices denominated in domestic currencies into USD. Research limitations/implications Because of the discontinuity of data for some daily return series, estimation with dynamic variance is not applicable. It is assumed that the error item follows normal distribution with constant variance. Originality/value The day-of-the-week effects are wide-spread in Bitcoin markets, and they are not mainly caused by movements of foreign exchange rates. Actually, empirical findings in this study provide evidence for inefficiency of Bitcoin markets.
The Reality and Future of the Secure Mobile Cloud Computing (SMCC): Survey

Mobile communication and mobile computing has revolutionized the way subscribers across the globe leverage and use a variety of services on the go. Mobile devices have evolved from mere simple handheld mobile phones that enabled voice calls only a few years back to smartphone devices that enable the user to access applications and value-added services anytime, anyplace. All of these, together with the recent reigniting of cloud computing technology, has spearheaded into the opportunistic and economically viable Mobile Cloud Computing (MCC) as the next innovative technology and ultimately a utility wave such as electricity or water. But this has created huge loopholes in facilitating reliable, scalable, performance oriented, safe, secure and auditable trusted services at affordable prices and profitability. Thus, this research proposal aims to create a fully-fledged Mobile Commerce-based Safe Mobile Cloud Computing (MCSMCC) framework to specifically accommodate Safe MCC (SMCC) with economic pricing models and e-trading schemes. As for the safety measures comprising of security, privacy, trust, identity management, audit and digital forensics among other related measures to suit SMCC, the objectives are to define and specify the necessary processes and protocols for secure e-trading, These objectives are further complimented by further defining and specifying SMCC pricing schemes that will be fair and equitable to users and vendors alike, and encourage critical mass market uptake and usage with confidence. Both of these central objectives will be experimented through simulation and prototyping to test and verify their purpose, effectiveness and efficiency.
Setup-Free Secure Search on Encrypted Data: Faster and Post-Processing Free

Abstract We present a novel secure search protocol on data and queries encrypted with Fully Homomorphic Encryption (FHE). Our protocol enables organizations (client) to (1) securely upload an unsorted data array x = ( x [1], . . . , x [ n ]) to an untrusted honest-but-curious sever, where data may be uploaded over time and from multiple data-sources; and (2) securely issue repeated search queries q for retrieving the first element ( i *, x [ i *]) satisfying an agreed matching criterion i* = min { i ∈ [ n ] | IsMatch( x [ i ], q ) = 1 }, as well as fetching the next matching elements with further interaction. For security, the client encrypts the data and queries with FHE prior to uploading, and the server processes the ciphertexts to produce the result ciphertext for the client to decrypt. Our secure search protocol improves over the prior state-of-the-art for secure search on FHE encrypted data (Akavia, Feldman, Shaul (AFS), CCS’2018) in achieving: – Post-processing free protocol where the server produces a ciphertext for the correct search outcome with overwhelming success probability. This is in contrast to returning a list of candidates for the client to postprocess, or suffering from a noticeable error probability, in AFS. Our post-processing freeness enables the server to use secure search as a sub-component in a larger computation without interaction with the client. – Faster protocol: (a) Client time and communication bandwidth are improved by a log 2 n/ log log n factor. (b) Server evaluates a polynomial of degree linear in log n (compare to cubic in AFS), and overall number of multiplications improved by up to log n factor. (c) Employing only GF(2) computations (compare to GF( p ) for p ≫ in AFS) to gain both further speedup and compatibility to all current FHE candidates. – Order of magnitude speedup exhibited by extensive benchmarks we executed on identical hardware for implementations of ours versus AFS’s protocols. Additionally, like other FHE based solutions, our solution is setup-free: to outsource elements from the client to the server, no additional actions are performed on x except for encrypting it element by element (each element bit by bit) and uploading the resulted ciphertexts to the server.
Linear Queries Estimation with Local Differential Privacy

We study the problem of estimating a set of d linear queries with respect to some unknown distribution p over a domain [J] based on a sensitive data set of n individuals under the constraint of local differential privacy. This problem subsumes a wide range of estimation tasks, e.g., distribution estimation and d-dimensional mean estimation. We provide new algorithms for both the offline (non-adaptive) and adaptive versions of this problem. In the offline setting, the set of queries are fixed before the algorithm starts. In the regime where n < d^2/\log(J), our algorithms attain L_2 estimation error that is independent of d. For the special case of distribution estimation, we show that projecting the output estimate of an algorithm due to [Acharya et al. 2018] on the probability simplex yields an L_2 error that depends only sub-logarithmically on J in the regime where n < J^2/\log(J). Our bounds are within a factor of at most (\log(J))^{1/4} from the optimal L_2 error. These results show the possibility of accurate estimation of linear queries in the high-dimensional settings under the L_2 error criterion.In the adaptive setting, the queries are generated over d rounds; one query at a time. In each round, a query can be chosen adaptively based on all the history of previous queries and answers. We give an algorithm for this problem with optimal L_{\infty} estimation error (worst error in the estimated values for the queries w.r.t. the data distribution). Our bound matches a lower bound on the L_{\infty} error in the offline version of this problem [Duchi et al. 2013].
The role of mastery learning in an intelligent tutoring system: Principal stratification on a latent variable

Students in Algebra I classrooms typically learn at different rates and struggle at different points in the curriculum-a common challenge for math teachers.Cognitive Tutor Algebra I (CTA1), an educational computer program, addresses such student heterogeneity via what they term "mastery learning," where students progress from one section of the curriculum to the next by demonstrating appropriate "mastery" at each stage.However, when students are unable to master a section's skills even after trying many problems, they are automatically promoted to the next section anyway.Does promotion without mastery impair the program's effectiveness?At least in certain domains, CTA1 was recently shown to improve student learning on average in a randomized effectiveness study.This paper uses student log data from that study in a continuous principal stratification model to estimate the relationship between students' potential mastery and the CTA1 treatment effect.In contrast to extant principal stratification applications, a student's propensity to master worked sections here is never directly observed.Consequently we embed an item-response model, which measures students' potential mastery, within the larger principal stratification model.We find that the tutor may, in fact, be more effective for students who are more frequently promoted (despite unsuccessfully completing sections of the material).However, since these students are distinctive in their educational strength (as well as in other respects), it remains unclear whether this enhanced effectiveness can be directly attributed to aspects of the mastery learning program.
Curve25519 for the Cortex-M4 and Beyond

We present techniques for the implementation of a key exchange protocol and digital signature scheme based on the Curve25519 elliptic curve and its Edwards form, respectively, in resource-constrained ARM devices. A possible application of this work consists of TLS deployments in the ARM Cortex-M family of processors and beyond. These devices are located towards the lower to mid-end spectrum of ARM cores, and are typically used on embedded devices. Our implementations improve the state-of-the-art substantially by making use of novel implementation techniques and features specific to the target platforms.