Reviewerly/exHarmony huggingface dataset:
Feature dependence: A method for reconstructing actual causes in engineering failure investigations

Engineering failure investigations seek to reconstruct the actual causes of major engineering failures. The investigators need to establish the existence of certain past events and the actual causal relationships that these events bear to the failures in question. In this paper, I examine one method for reconstructing the actual causes of failure events, which I call "feature dependence". The basic idea of feature dependence is that some features of an event are informative about the features of its causes; therefore, the investigators can use the features of a known failure event to reconstruct details of its causes. I make explicit the structure of feature dependence and the evidential basis of its key premises, and show how feature dependence works in the investigation of the American Airlines Flight 191 accident.
HYBRID MODEL FOR THE CLASSIFICATION OF QUESTIONS EXPRESSED IN NATURAL LANGUAGE

Question-answering systems rely on an unstructured text corpora or a knowledge base to answer user questions. Most of these systems store knowledge in multiple repositories including RDF. To access this type of repository, SPARQL is the most convenient formal language. It is a complex language, it is therefore necessary to transform the questions expressed in natural language by users into a SPARQL query. As this language is complex, several approaches have been proposed to transform the questions expressed in natural language by users into a SPARQL query.However, the identification of the question type is a serious problem. Questions classification plays a potential role at this level. Machine learning algorithms including neural networks are used for this classification. With the increase in the volume of data, neural networks better perform than those obtained by machine learning algorithms, in general. That is, neural networks, machine learning algorithms also remain good classifiers. For more efficiency, a combination of convolutional neural network with these algorithms has been suggested in this paper. The BICNN-SVM combination has obtained good score not only with small dataset with a precision of 96.60% but also with a large dataset with 94.05%.
Use of Neural Topic Models in conjunction with Word Embeddings to extract meaningful topics from short texts

Unsupervised machine learning is utilized as a part of the process of topic modeling to discover dormant topics hidden within a large number of documents. The topic model can help with the comprehension, organization, and summarization of large amounts of text. Additionally, it can assist with the discovery of hidden topics that vary across different texts in a corpus. Traditional topic models like pLSA (probabilistic latent semantic analysis) and LDA suffer performance loss when applied to short-text analysis caused by the lack of word co-occurrence information in each short text. One technique being developed to solve this problem is pre-trained word embedding (PWE) with an external corpus used with topic models. These techniques are being developed to perform interpretable topic modeling on short texts. Deep neural networks (DNN) and deep generative models have recently advanced, allowing neural topic models (NTM) to achieve flexibility and efficiency in topic modeling. There have been few studies on neural-topic models with pre-trained word embedding for producing significant topics from short texts. An extensive study with five NTMs was accomplished to test the efficacy of additional PWE in generating comprehensible topics through experiments with different datasets in Arabic and French concerning Moroccan news published on Facebook pages. Several metrics, including topic coherence and topic diversity, are utilized in the process of evaluating the extracted topics. Our research shows that the topic coherence of short texts can be significantly improved using a word embedding with an external corpus.
Software Requirements Classification using Deep-learning Approach with Various Hidden Layers

Software requirement classification is becoming increasingly crucial for the industry to keep up with the demand of growing project sizes.Based on client feedback or demand, software requirement classification is critical in segregating user needs into functional and quality requirements.However, because there are numerous machine learning (ML) and deep-learning (DL) models that require parameter tuning, the use of ML to facilitate decision-making across the software engineering pipeline is not well understood.Five distinct word embedding techniques were applied to the functional and quality software requirements in this study.The imbalanced classes in the dataset are balanced using Synthetic Minority Oversampling technique (SMOTE).Then, to reduce duplicate and unnecessary features, feature selection and dimensionality reduction techniques are used.Dimensionality reduction is accomplished with Principal Component Analysis (PCA), while feature selection is accomplished with the Rank-Sum Test (RST).For binary categorization into functional and non-functional needs, the generated vectors are provided as inputs to eight distinct Deep Learning classifiers.The findings of the research show that using a combination of word embedding and feature selection techniques in conjunction with various classifiers can accurately classify functional and quality software requirements.
Adoption of Cloud Computing in UAE

With the advent of new technology, the IT industry continuously strives to innovate in terms of deploying products or providing services and Cloud Computing is rapidly moving in the hype cycle. With practically all the service providers offering products and services with cloud features and functionality and investing in creating a cloud computing ecosystem, it has become important to understand what these ecosystem means to the organizations based out of UAE who have to decide whether to adopt cloud computing or shun it. There are several factors impacting an organization's decision on its choice of cloud computing adoption (like data security, Legal implications and derived benefits especially in UAE) (Al Tamimi &amp; Company, 2005). There is a need for an assessment of cloud ecosystem in UAE which would be one of the significant factors considered cloud adoption in this region. This study analyzes the current cloud ecosystem providers in UAE and their product and services on cloud computing. It also tries to relate the relation between the cloud ecosystem and the factors impacting organization's decision to adopt cloud computing.
Analysis of the comments on “Identity-Based Distributed Provable Data Possession in Multicloud Storage”

In 2016, Peng et al. pointed out in the literature (IEEE Transactions on Services Computing, Vol. 9, No. 6, pp. 996-998, Nov./Dec. 2016) that Wang's identity-based provable data possessing scheme ID-DPDP(IEEE Transactions on Services Computing, vol. 8, no. 2, pp. 328-340, Mar./Apr. 2015) has security vulnerability, and Peng et al. offered the remedy solutions. Unfortunately, we demonstrate that Peng et al.'s method also has a security loophole that malicious cloud servers can produce valid proof information without original data. Accordingly, we provide a revised solution to the problem while preserving the security features of the ID-DPDP.
Operator Mutexes and Symmetries for Simplifying Planning Tasks

Simplifying classical planning tasks by removing operators while preserving at least one optimal solution can significantly enhance the performance of planners. In this paper, we introduce the notion of operator mutex, which is a set of operators that cannot all be part of the same (strongly) optimal plan. We propose four different methods for inference of operator mutexes and experimentally verify that they can be found in a sizable number of planning tasks. We show how operator mutexes can be used in combination with structural symmetries to safely remove operators from the planning task.
Nano Ferromagnetism: Single Domain 10 nm Ferromagnetism Imprinted on Superparamagnetic Nanoparticles Using Chiral Molecules (Small 1/2019)

In article number 1804557, Danny Porath, Yossi Paltiel, and co-workers demonstrate imprinting of ferromagnetism on super paramagnetic iron oxide nanoparticles (SPIONs) following asymmetric self-assembled adsorption of polyalanine chiral molecules. The adsorbed chiral molecules stabilize the magnetization direction of SPIONs at room temperature with a coercive field of about 80 Oe. This procedure allows reduction in size of magnetic memory systems.
Quantitative Security Assurance

Security assurance is the confidence that a system meets its security requirements and is resilient against security vulnerabilities and failures. Existing approaches can be characterized as (1) qualitative in nature, (2) tend to achieve their goals manually to a large extent, (3) very costly, (4) development-process oriented, and finally, (3) treat all security requirements within one domain equally for all applications regardless of the context. In this chapter, the authors propose a security assurance framework and its assurance evaluation process. The framework and process depend on a quantitative security assurance metrics that were developed too. The proposed metric considers both the security requirements and vulnerability. Weight has been introduced to the security requirement metric to measure the importance of security requirements that need to be fulfilled. The framework with the proposed quantitative assurance metrics are evaluated and validated using two field case studies related to two operational REST APIs that belong to and are used by Statistics Norway.
Detection of False Positive Situation in Review Mining

As the Internet is evolving at a steeper rate, reviews related to a product have become a vital data which help users to make informed decisions. Users are totally dependent upon those reviews given by customers with the experience they felt and makers depend on these user-generated reviews to apprehend the sentiments of users related to a product. Henceforth, it is mandatory for both makers and users to create a portal where customers can peruse all the reviews in a comprehensive manner in a less amount of time. Considering this, a predictive model is developed that detects false positive reviews from original reviews and ratings are calculated to judge how these fake reviews create confusion in the mind of customers.
An Overview of Deep Learning and Its Applications

Deep learning is the machine learning method that changed the field of artificial intelligence in the last five years. In the view of industrial research, this technology is disruptive: It considerably pushes the border of tasks that can be automated, changes the way applications are developed, and is available to virtually everyone.
Privacy Sustaining Constant Length Ciphertext-Policy Attribute-Based Broadcast Encryption

Cryptography-based data distribution is an advanced technique used to control the access of broadcasting data over cloud storage environment. A ciphertext-policy attribute-based encryption (CP-ABE) technique encrypts the data in the cloud scenario, and the data is accessed only when the decryptor satisfies the encrypted access scheme. In this technique, each user is assigned with attributes in the recovering key, and the authorized user is allowed to process the data when their attributes match the keywords embedded in the access policy. Existing techniques used for providing privacy partially hides the access policy in the ciphertext. Therefore, it may leak private information about the user; also, the size of ciphertext is linear with respect to the sum of attributes. In this paper, an efficient privacy sustaining ciphertext-policy attribute-based broadcast encryption technique with AND gate and wildcard access scheme is proposed. The proposed technique provides security proof to sensitive data broadcasted over unsecured channel with hidden access policy. Further, this technique will achieve constant length ciphertext for any number of attributes. Moreover, the communication overhead and the computation complexity are significantly reduced in the proposed method.
Towards a Theory of Analytical Behaviour: A Model of Decision-Making in Visual Analytics

This paper introduces a descriptive model of the human-computer processes that lead to decision-making in visual analytics.A survey of nine models from the visual analytics and HCI literature are presented to account for different perspectives such as sense-making, reasoning, and low-level human-computer interactions.The survey examines the people and computers (entities) presented in the models, the divisions of labour between entities (both physical and role-based), the behaviour of both people and machines as constrained by their roles and agency, and finally the elements and processes which define the flow of data both within and between entities.The survey informs the identification of four observations that characterise analytical behaviour -defined as decision-making facilitated by visual analytics: bilateral discourse, divisions of labour, mixed-synchronicity information flows, and bounded behaviour.Based on these principles, a descriptive model is presented as a contribution towards a theory of analytical behaviour.The future intention is to apply prospect theory, a economic model of decision-making under uncertainty, to the study of analytical behaviour.It is our assertion that to apply prospect theory first requires a descriptive model of the processes that facilitate decision-making in visual analytics.We conclude it necessary to measure the perception of risk in future work in order to apply prospect theory to the study of analytical behaviour using our proposed model.
FPGA Implementation of Speculative Prefix Accumulation-Driven RNS for High-Performance FIR Filter

In this paper, we present speculation-driven prefix topology-based finite impulse response (FIR) filters design using residue number system (RNS) arithmetic. The core objective of our proposed framework is to maximize the prefix accumulation in the application of RNS to the design high-performance FIR filter design. To achieve this, we propose a RAM-based reverse conversion model followed by accumulation to produce the modular multiplication. The proposed RNS design makes use of block RAMs available in FPGA devices and appropriate moduli sets in order to accommodate FIR convolution results. The proposed approach is formulated to design precomputed reverse converters for different moduli sets and to implement FPGA as target devices. As a result, we propose speculative parallel prefix topology-based post accumulation technique for RNS-based multiplication, along with a high-performance FIR filter architecture that employs independent modulo channel RNS arithmetic. Experiment results of RNS-FIR design over different number of FIR taps and input operand word lengths alongside with appropriate moduli sets that suits to accommodate FIR end results.
Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in CNNs

While CNNs naturally lend themselves to densely sampled data, and sophisticated implementations are available, they lack the ability to efficiently process sparse data. In this work we introduce a suite of tools that exploit sparsity in both the feature maps and the filter weights, and thereby allow for significantly lower memory footprints and computation times than the conventional dense framework, when processing data with a high degree of sparsity. Our scheme provides (i) an efficient GPU implementation of a convolution layer based on direct, sparse convolution; (ii) a filter step within the convolution layer, which we call attention, that prevents fill-in, i.e., the tendency of convolution to rapidly decrease sparsity, and guarantees an upper bound on the computational resources; and (iii) an adaptation of back-propagation that makes it possible to combine our approach with standard learning frameworks, while still exploiting sparsity in the data and the model.
Learning Spatiotemporal 3D Convolution with Video Order Self-supervision

The purpose of this work is to explore self-supervised learning (SSL) strategy to capture a better feature with spatiotemporal 3D convolution. Although one of the next frontier in video recognition must be spatiotemporal 3D CNN, the convergence of the 3D convolutions is really difficult because of their enormous parameters or missing temporal(motion) feature. One of the effective solutions is to collect a $$10^5$$ -order video database such as Kinetics/Moments in Time. However, this is not an efficient with burden of manual annotations. In the paper, we train 3D CNN on wrong video-sequence detection tasks in a self-supervised manner (without any manual annotation). The shuffling and verification of consecutive video-frame-order is effective for 3D CNN to capture temporal feature and get a good start point of parameters to be fine-tuned. In the experimental section, we verify that our pretrained 3D CNN on wrong clip detection improves the level of performance on UCF101 ( $$+3.99\%$$ better than baseline, namely training 3D convolution from scratch).
Datasets column

Affective video content analysis aims at the automatic recognition of emotions elicited by videos. It has a large number of applications, including mood based personalized content recommendation, video indexing, and efficient movie visualization and browsing. Beyond the analysis of existing video material, affective computing techniques can also be used to generate new content, e.g., movie summarization, personalized soundtrack recommendation to make user-generated videos more attractive. Affective techniques can furthermore be used to enhance the user engagement with advertising content by optimizing the way ads are inserted inside videos.
Impact of Server Thermal Design on the Cooling Efficiency: Chassis Design

There are various designs for segregating hot and cold air in data centers such as cold aisle containment (CAC), hot aisle containment (HAC), and chimney exhaust rack. These containment systems have different characteristics and impose various conditions on the information technology equipment (ITE). One common issue in HAC systems is a pressure build-up inside the HAC (known as backpressure). Backpressure also can be present in CAC systems in case of airflow imbalances. Hot air recirculation, limited cooling airflow rate in servers, and reversed flow through ITE with weaker fan systems (e.g., network switches) are some known consequences of backpressure. Currently, there is a lack of experimental data on the interdependency between overall performance of ITE and its internal design when backpressure is imposed on ITE. In this paper, three commercial 2-rack unit (RU) servers with different internal designs from various generations and performance levels are tested and analyzed under various environmental conditions. Smoke tests and thermal imaging are implemented to study the airflow patterns inside the tested equipment. In addition, the impact of hot air leakage into the servers through chassis perforations on the fan speed and the power consumption of the servers are studied. Furthermore, the cause of the discrepancy between measured inlet temperatures by the intelligent platform management interface (IPMI) and external sensors is investigated. It is found that arrangement of fans, segregation of space upstream and downstream of fans, leakage paths, the location of baseboard management controller (BMC) sensors, and the presence of backpressure can have a significant impact on ITE power and cooling efficiency.
Rapid estimation of earthquake locations using waveform traveltimes

SUMMARY We introduce a new approach for locating earthquakes using arrival times derived from waveforms. The most costly computational step of the algorithm scales as the number of stations in the active seismographic network. In this approach, a variation on existing grid search methods, a series of full waveform simulations are conducted for all receiver locations, with sources positioned successively at each station. The traveltime field over the region of interest is calculated by applying a phase picking algorithm to the numerical wavefields produced from each simulation. An event is located by subtracting the stored traveltime field from the arrival time at each station. This provides a shifted and time-reversed traveltime field for each station. The shifted and time-reversed fields all approach the origin time of the event at the source location. The mean or median value at the source location thus approximates the event origin time. Measures of dispersion about this mean or median time at each grid point, such as the sample standard error and the average deviation, are minimized at the correct source position. Uncertainty in the event position is provided by the contours of standard error defined over the grid. An application of this technique to a synthetic data set indicates that the approach provides stable locations even when the traveltimes are contaminated by additive random noise containing a significant number of outliers and velocity model errors. It is found that the waveform-based method out-performs one based upon the eikonal equation for a velocity model with rapid spatial variations in properties due to layering. A comparison with conventional location algorithms in both a laboratory and field setting demonstrates that the technique performs at least as well as existing techniques.
Zero-sum Distinguishers for Round-reduced GIMLI Permutation

GIMLI is a 384-bit permutation proposed by Bernstein et al. at CHES 2017. It is designed with the goal of achieving both high security and high performance across a wide range of hardware and software platforms. Since GIMLI can be used as a building block for many cryptographic schemes, it is important to understand its concrete security. To the best of our knowledge, third party cryptanalysis of GIMLI is limited. In this paper, we identify some zero-sum distinguishers for 14-round GIMLI with the inside-out technique, which are one-round longer than the integral distinguishers presented by the designers. Although we obtain improved cryptanalysis results, these zero-sum distinguishers are far from threatening the full version of GIMLI.
Systematic Comprehension for Developer Reply in Mobile System Forum

Review-based software development has become increasingly prevalent in recent years. Existing efforts aiming at either informative evaluation or sentiment analysis are mainly from the perspective of the reviewers, while neglecting the attitude and behavior of the developers. Such efforts inevitably suffer from recommendation bias in practice, and thus benefit little for the improvement of user reviews.In this paper, we attempt to bridge the gap between user review and developer reply, and conduct a systematic study for review reply in development forums, especially in Chinese mobile system forums. To this end, we concentrate on three research questions: 1) should a targeted review be replied; 2) how long time it should be replied; 3) does traditional review analysis help to pursue a reply for certain review? To answer such questions, given certain review datasets, we perform a systematical study including the following three stages: 1) a binary classification for reply behavior prediction, 2) a regression for prediction of reply time, 3) a systematic factor study for the relationship between traditional review analysis and reply performance. To enhance the accuracy of prediction and analysis, we proposed a CNN-based weak-supervision analysis framework, which exploits manifold techniques from NLP and deep learning. We validate our approach via extensive comparison experiments. The results show that our analysis framework is effective. More importantly, we have uncovered several interesting findings, which provide valuable guidance for further review improvement and recommendation.