Reviewerly/exHarmony huggingface dataset:
Application of Extreme Learning Machine for Predicting Chlorophyll-a Concentration Inartificial Upwelling Processes

Artificial upwelling, artificially pumping up nutrient-rich ocean waters from deep to surface, is increasingly applied to stimulating phytoplankton activity. As a proxy for the amount of phytoplankton present in the ocean, the concentration of chlorophyll a ( chl-a) may be influenced by water physical factors altered in artificial upwelling processes. However, the accuracy and convenience of measuring chl-a are limited by present technologies and equipment. Our research intends to study the correlations between chl-a concentration and five water physical factors, i.e., salinity, temperature, depth, dissolved oxygen (DO), and pH, possibly affected by artificial upwelling. In this paper, seven models are presented to predict chl-a concentration, respectively. Two of them are based on traditional regression algorithms, i.e., multiple linear regression (MLR) and multivariate quadratic regression (MQR), while five are based on intelligent algorithms, i.e., backpropagation-neural network (BP-NN), extreme learning machine (ELM), genetic algorithm-ELM (GA-ELM), particle swarm optimization-ELM (PSO-ELM), and ant colony optimization-ELM (ACO-ELM). These models provide a quick prediction to study the concentration of chl-a . With the experimental data collected from Xinanjiang Experiment Station in China, the results show that chl-a concentration has a strong correlation with salinity, temperature, DO, and pH in the process of artificial upwelling and PSO-ELM has the best overall prediction ability.
Detection of Trace Elements/Isotopes in Olympic Dam Copper Concentrates by nanoSIMS

Many analytical techniques for trace element analysis are available to the geochemist and geometallurgist to understand and, ideally, quantify the distribution of trace and minor components in a mineral deposit. Bulk trace element data are useful, but do not provide information regarding specific host minerals—or lack thereof, in cases of surface adherence or fracture fill—for each element. The CAMECA nanoscale secondary ion mass spectrometer (nanoSIMS) 50 and 50L instruments feature ultra-low minimum detection limits (to parts-per-billion) and sub-micron spatial resolution, a combination not found in any other analytical platform. Using ore and copper concentrate samples from the Olympic Dam mining-processing operation, South Australia, we demonstrate the application of nanoSIMS to understand the mineralogical distribution of potential by-product and detrimental elements. Results show previously undetected mineral host assemblages and elemental associations, providing geochemists with insight into mineral formation and elemental remobilization—and metallurgists with critical information necessary for optimizing ore processing techniques. Gold and Te may be seen associated with brannerite, and Ag prefers chalcocite over bornite. Rare earth elements may be found in trace quantities in fluorapatite and fluorite, which may report to final concentrates as entrained liberated or gangue-sulfide composite particles. Selenium, As, and Te reside in sulfides, commonly in association with Pb, Bi, Ag, and Au. Radionuclide daughters of the 238U decay chain may be located using nanoSIMS, providing critical information on these trace components that is unavailable using other microanalytical techniques. These radionuclides are observed in many minerals but seem particularly enriched in uranium minerals, some phosphates and sulfates, and within high surface area minerals. The nanoSIMS has proven a valuable tool in determining the spatial distribution of trace elements and isotopes in fine-grained copper ore, providing researchers with crucial evidence needed to answer questions of ore formation, ore alteration, and ore processing.
Multi-UAV Mission Allocation under Constraint

This paper is devoted to the unmanned aerial vehicle (UAV) mission allocation problem. To solve this problem in a more realistic battlefield environment, an improved mathematical model for UAV mission allocation is proposed. Being different from previous formulations, this model not only considers the difference in the importance of the target but also the constraints of the time window. In addition, an indicator of reconnaissance reward is added to this model. Each target area has a different importance, just as the strategic value of each region is different in combat. In this paper, we randomly generate the value factor for each reconnaissance area. To solve the mathematical model with different operational intentions, a dimensionality reduction process for which the reconnaissance reward is the optimization objective is presented. Finally, based on the improved model, the simulation result with Lingo is compared with that of non-dominated sorting genetic algorithm with elite strategy (NSGA-II) and genetic algorithm (GA) to verify the reliability and the effectiveness of the improved method.
Contributions of Micro, Small and Medium Enterprises (MSMEs) to Income Generation, Employment and GDP: Case Study Ethiopia

The pillar goals of this research are to review the conditions of MSMEs, their contribution to employment creation, income generation, poverty alleviation, contributions to the local, regional and national GDP, stimulating entrepreneurial climate and the challenges and opportunities in the design, implementations, marketing opportunities, linkages, financial sources, dynamics, survival and policy landscape. To achieve the presented purposes, we collected primary and secondary data through a survey, focus group discussions and documents reviews. We used qualitative and quantitative approaches to analyse the collected data using various statistical programs. &#x0D; &#x0D; We used descriptive and econometric statistical analysis to process the data, obtain the relevant estimation results and fully discuss the purposes under the study. We firmly maintain that the systems we presented, and the methods applied enabled us to tackle the aims of the study. MSMEs in Ethiopian are the chief sources of job, income, significantly contribute to the local, regional and national GDP and key policies to eliminate poverty. &#x0D; &#x0D; In the log-linear regression, we found that MSMEs initial capital, BDS, access to credit facility are the key determinants of MSMEs performance. Majority of the MSMEs produce for local and regional markets; few for national markets and none for international markets. Besides, we found that sex of MSMEs owner/manager, BDS, access to credit and capital size strongly determine the survival of MSMEs.&#x0D; &#x0D; Based on this study, the major obstacles of MSMEs in Ethiopia are the question of sustainability, lack of credit, weak market linkage, insufficient training, weak human resources development schemes, dependency on government and spoon-feeding mentality, oscillations in government policies, price variations, weak links and poor market and product development strategies.
Lower Bounds for Oblivious Data Structures

An oblivious data structure is a data structure where the memory access patterns reveals no information about the operations performed on it. Such data structures were introduced by Wang et al. [ACM SIGSAC'14] and are intended for situations where one wishes to store the data structure at an untrusted server. One way to obtain an oblivious data structure is simply to run a classic data structure on an oblivious RAM (ORAM). Until very recently, this resulted in an overhead of ω(lg n) for the most natural setting of parameters. Moreover, a recent lower bound for ORAMs by Larsen and Nielsen [CRYPTO'18] show that they always incur an overhead of at least Ω(lg n) if used in a black box manner. To circumvent the ω(lg n) overhead, researchers have instead studied classic data structure problems more directly and have obtained efficient solutions for many such problems such as stacks, queues, deques, priority queues and search trees. However, none of these data structures process operations faster than Θ(lg n), leaving open the question of whether even faster solutions exist. In this paper, we rule out this possibility by proving Ω(lg n) lower bounds for oblivious stacks, queues, deques, priority queues and search trees.
The Language of Legal and Illegal Activity on the Darknet

The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity.Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools.However, little is known about what characteristics texts communicated through the Darknet have, and how well off-the-shelf NLP tools do on this domain.This paper tackles this gap and performs an in-depth investigation of the characteristics of legal and illegal text in the Darknet, comparing it to a clear net website with similar content as a control condition.Taking drug-related websites as a test case, we find that texts for selling legal and illegal drugs have several linguistic characteristics that distinguish them from one another, as well as from the control condition, among them the distribution of POS tags, and the coverage of their named entities in Wikipedia.
Near-Hashing-Bound Multiple-Rate Quantum Turbo Short-Block Codes

Quantum stabilizer codes (QSCs) suffer from a low quantum coding rate since they have to recover the quantum bits (qubits) in the face of both bit-flip and phase-flip errors.In this treatise, we conceive a low-complexity concatenated quantum turbo code (QTC) design exhibiting a high quantum coding rate.The high quantum coding rate is achieved by combining the quantum-domain version of shortblock codes (SBCs) also known as single parity check (SPC) codes as the outer codes and quantum unity-rate codes (QURCs) as the inner codes.Despite its design simplicity, the proposed QTC yields a near-hashingbound error correction performance.For instance, compared to the best half-rate QTC known in the literature, namely the QIrCC-QURC scheme, which operates at the distance of D = 0.037 from the quantum hashing bound, our novel QSBC-QURC scheme can operate at the distance of D = 0.029.In addition, it is worth mentioning that this is the first instantiation of QTCs capable of adjusting the quantum encoders according to the quantum coding rate required for mitigating the Pauli errors, given the different depolarizing probabilities of the quantum channel.
Vitro

This paper investigates whether voice assistants can play a useful role in the specialized work-life of the knowledge worker (in a biology lab). It is motivated both by promising advances in voice-input technology, and a long-standing vision in the community to augment scientific processes with voice-based agents. Through a reflection on our design process and a limited but fully functional prototype, Vitro, we find that scientists wanted a voice-enabled device that acted not a lab assistant, but lab equipment. Second, we discovered that such a device would need to be deeply embedded in the physical and social space in which it served scientists. Finally, we discovered that scientists preferred a device that supported their practice of "careful deviation" from protocols in their lab work. Through this research, we contribute implications for the design of voice-enabled systems in workplace settings.
Processor Assisted Worklist Scheduling for FPGA Accelerated Graph Processing on a Shared-Memory Platform

FPGA-based processing has gained much attention for accelerating graph analytics because of the demand in performance and energy efficiency. However, while priority scheduling has been shown to be an effective optimization for improving performance for worklist-based graph computations, it is rarely used in accelerator designs due to its implementation complexity and memory-access overhead. In this paper, we present a heterogeneous processing approach for priority scheduling on a shared-memory CPU-FPGA platform. By exploiting the closely-coupled integration of the host processor and the FPGA accelerator, our system dynamically offloads the task of scheduling to a software scheduler on the processor for its programmability, high-capacity cache and low memory latency, while the FPGA graph processing accelerator enjoys the scheduling benefit and delivers higher performance at excellent energy efficiency. To understand the effectiveness of our solution, we compared it with FPGA-only solutions for two scheduling schemes: the well-known Dijkstra scheduling for Single Source Shortest Path and a new scheduling optimization we developed for improving the data locality of Breadth First Search. Whereas the FPGA-only solution requires an impractical amount of on-chip storage to implement a priority queue, the proposed processor-assisted scheduling that moves the task of scheduling to the processor consumes a negligible load on the processor and retains most of the performance benefit from priority scheduling.
Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension

Comprehending multi-turn spoken conversations is an emerging research area, presenting challenges different from reading comprehension of passages due to the interactive nature of information exchange from at least two speakers.Unlike passages, where sentences are often the default semantic modeling unit, in multi-turn conversations, a turn is a topically coherent unit embodied with immediately relevant context, making it a linguistically intuitive segment for computationally modeling verbal interactions.Therefore, in this work, we propose a hierarchical attention neural network architecture, combining turnlevel and word-level attention mechanisms, to improve spoken dialogue comprehension performance.Experiments are conducted on a multi-turn conversation dataset, where nurses inquire and discuss symptom information with patients.We empirically show that the proposed approach outperforms standard attention baselines, achieves more efficient learning outcomes, and is more robust to lengthy and out-of-distribution test samples.
Unsupervised Controllable Text Formalization

We propose a novel framework for controllable natural language transformation. Realizing that the requirement of parallel corpus is practically unsustainable for controllable generation tasks, an unsupervised training scheme is introduced. The crux of the framework is a deep neural encoder-decoder that is reinforced with text-transformation knowledge through auxiliary modules (called scorers). These scorers, based on off-the-shelf language processing tools, decide the learning scheme of the encoder-decoder based on its actions. We apply this framework for the text-transformation task of formalizing an input text by improving its readability grade; the degree of required formalization can be controlled by the user at run-time. Experiments on public datasets demonstrate the efficacy of our model towards: (a) transforming a given text to a more formal style, and (b) varying the amount of formalness in the output text based on the specified input control. Our code and datasets are released for academic use.
Refined PHD Filter for Multi-Target Tracking under Low Detection Probability

Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade significantly under low detection probability in practice, especially when continuous miss detection occurs. Based on sequential Monte Carlo implementation of Probability Hypothesis Density (PHD) filter, this paper proposes a heuristic method called the Refined PHD (R-PHD) filter to improve multi-target tracking performance under low detection probability. In detail, this paper defines a survival probability which is dependent on target state, and labels individual extracted targets and corresponding particles. When miss detection occurs due to low detection probability, posterior particle weights will be revised according to the prediction step. Finally, we transform the target confirmation problem into a hypothesis test problem, and utilize sequential probability ratio test to distinguish real targets and false alarms in real time. Computer simulations with respect to different detection probabilities, average numbers of false alarms and continuous miss detection durations are provided to corroborate the superiority of the proposed method, compared with standard PHD filter, Cardinalized PHD (CPHD) filter and Cardinality Balanced Multi-target Multi-Bernoulli (CBMeMBer) filter.
DL-LA: Deep Learning Leakage Assessment

In recent years, deep learning has become an attractive ingredient to side-channel analysis (SCA) due to its potential to improve the success probability or enhance the performance of certain frequently executed tasks. One task that is commonly assisted by machine learning techniques is the profiling of a device’s leakage behavior in order to carry out a template attack. At CHES 2019, deep learning has also been applied to non-profiled scenarios for the first time, extending its reach within SCA beyond template attacks. The proposed method, called DDLA, has some tempting advantages over traditional SCA due to merits inherited from (convolutional) neural networks. Most notably, it greatly reduces the need for pre-processing steps&lt; when the SCA traces are misaligned or when the leakage is of a multivariate nature. However, similar to traditional attack scenarios the success of this approach highly depends on the correct choice of a leakage model and the intermediate value to target. In this work we explore, for the first time in literature, whether deep learning can similarly be used as an instrument to advance another crucial (non-profiled) discipline of SCA which is inherently independent of leakage models and targeted intermediates, namely leakage assessment. In fact, given the simple classification-based nature of common leakage assessment techniques, in particular distinguishing two groups fixed-vs-random or fixed-vs-fixed, it comes as a surprise that machine learning has not been brought into this context, yet. Our contribution is the development of the first full leakage assessment methodology based on deep learning. It gives the evaluator the freedom to not worry about location, alignment and statistical order of the leakages and easily covers multivariate and horizontal patterns as well. We test our approach against a number of case studies based on FPGA, ASIC and μC implementations of the PRESENT block cipher, equipped with state-of-the-art SCA countermeasures. Our results clearly show that the proposed methodology and network structures are robust across all case studies and outperform the classical detection approaches (t-test and X2-test) in all considered scenarios.
A Solution for the Risk of Non-deterministic Transactions in Hyperledger Fabric

Blockchain is one of the most popular distributed ledger technologies. In order to solve the trustless problems between enterprises, many permissioned blockchain platforms are proposed in recent years. Hyperledger Fabric is a permissioned blockchain which is comprised of chaincode, endorsing peers, ordering service, committing peers and membership service. Fabric provides identity authentication, transactions encryption and high performance. Hence, decentralized application based on Fabric can be applied among enterprises to implement the immutable and high throughput for transactions. However, some limitations still exist in Fabric framework. This paper surveys the risk of non-deterministic transactions in Fabric, which can be caused by read-write conflict and transactions order dependency. Transactions are judged to be invalid by committing peers when read-write conflict exists. Besides, transactions submission order may be disrupted during endorsing and ordering. Both of the two risks can make the final transaction execution result non-deterministic. Finally, a solution of cache layer on Fabric client is proposed to solve the risk of non-deterministic transactions.
Impact of Discretization Noise of the Dependent Variable on Machine Learning Classifiers in Software Engineering

Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may introduce noise (i.e., discretization noise) due to ambiguous class loyalty of data points that are close to the artificial threshold. Previous studies do not provide a clear directive on the impact of discretization noise on the classifiers and how to handle such noise. In this paper, we propose a framework to help researchers and practitioners systematically estimate the impact of discretization noise on classifiers in terms of its impact on various performance measures and the interpretation of classifiers. Through a case study of 7 software engineering datasets, we find that: 1) discretization noise affects the different performance measures of a classifier differently for different datasets; 2) Though the interpretation of the classifiers are impacted by the discretization noise on the whole, the top 3 most important features are not affected by the discretization noise. Therefore, we suggest that practitioners and researchers use our framework to understand the impact of discretization noise on the performance of their built classifiers and estimate the exact amount of discretization noise to be discarded from the dataset to avoid the negative impact of such noise.
From “Welcome New Gabbers” to the Pittsburgh Synagogue Shooting: The Evolution of Gab

Gab, an online social media platform with very little content moderation, has recently come to prominence as an alt-right community and a haven for hate speech. We document the evolution of Gab since its inception until a Gab user carried out the most deadly attack on the Jewish community in US history. We investigate Gab language use, study how topics evolved over time, and find that the shooters’ posts were among the most consistently anti-Semitic on Gab, but that hundreds of other users were even more extreme.
Automating the Capture of Structured Pathology Data for Prostate Cancer Clinical Care and Research

Purpose Cancer pathology findings are critical for many aspects of care but are often locked away as unstructured free text. Our objective was to develop a natural language processing (NLP) system to extract prostate pathology details from postoperative pathology reports and a parallel structured data entry process for use by urologists during routine documentation care and compare accuracy when compared with manual abstraction and concordance between NLP and clinician-entered approaches. Materials and Methods From February 2016, clinicians used note templates with custom structured data elements (SDEs) during routine clinical care for men with prostate cancer. We also developed an NLP algorithm to parse radical prostatectomy pathology reports and extract structured data. We compared accuracy of clinician-entered SDEs and NLP-parsed data to manual abstraction as a gold standard and compared concordance (Cohen’s κ) between approaches assuming no gold standard. Results There were 523 patients with NLP-extracted data, 319 with SDE data, and 555 with manually abstracted data. For Gleason scores, NLP and clinician SDE accuracy was 95.6% and 95.8%, respectively, compared with manual abstraction, with concordance of 0.93 (95% CI, 0.89 to 0.98). For margin status, extracapsular extension, and seminal vesicle invasion, stage, and lymph node status, NLP accuracy was 94.8% to 100%, SDE accuracy was 87.7% to 100%, and concordance between NLP and SDE ranged from 0.92 to 1.0. Conclusion We show that a real-world deployment of an NLP algorithm to extract pathology data and structured data entry by clinicians during routine clinical care in a busy clinical practice can generate accurate data when compared with manual abstraction for some, but not all, components of a prostate pathology report.