Reviewerly/exHarmony huggingface dataset:
An impact of time and item influencer in collaborative filtering recommendations using graph-based model

Recommender Systems deal with the issue of overloading information by retrieving the most relevant sources in the wide range of web services. They help users by predicting their interests in many domains like e-government, social networks, e-commerce and entertainment. Collaborative Filtering (CF) is the most promising technique used in recommender systems to give suggestions based on liked-mind users’ preferences. Despite the widespread use of CF in providing personalized recommendation, this technique has problems including cold start, data sparsity and gray sheep. Eventually, these problems lead to the deterioration of the efficiency of CF. Most existing recommendation methods have been proposed to overcome the problems of CF. However, they fail to suggest the top-n recommendations based on the sequencing of the users’ priorities. In this research, to overcome the shortcomings of CF and current recommendation methods in ranking preference dataset, we have used a new graph-based structure to model the users’ priorities and capture the association between users and items. Users’ profiles are created based on their past and current interest. This is done because their interest can change with time. Our proposed algorithm keeps the preferred items of active user at the beginning of the recommendation list. This means these items come under top-n recommendations, which results in satisfaction among users. The experimental results demonstrate that our algorithm archives the significant improvement in comparison with CF and other proposed recommendation methods in terms of recall, precision, f-measure and MAP metrics using two benchmark datasets including MovieLens and Superstore.
Quantum Transducer Using a Parametric Driven-Dissipative Phase Transition

We study a dissipative Kerr-resonator subject to both single- and two-photon detuned drives. Beyond a critical detuning threshold, the Kerr resonator exhibits a semiclassical first-order dissipative phase transition between two different steady-states, that are characterized by a $\pi$ phase switch of the cavity field. This transition is shown to persist deep into the quantum limit of low photon numbers. Remarkably, the detuning frequency at which this transition occurs depends almost-linearly on the amplitude of the single-photon drive. Based on this phase switching feature, we devise a sensitive quantum transducer that translates the observed frequency of the parametric quantum phase transition to the detected single-photon amplitude signal. The effects of noise and temperature on the corresponding sensing protocol are addressed and a realistic circuit-QED implementation is discussed.
Automating the Paris System for urine cytopathology—A hybrid deep‐learning and morphometric approach

The Paris System for Urine Cytopathology (the Paris System) has succeeded in making the analysis of liquid-based urine preparations more reproducible. Any algorithm seeking to automate this system must accurately estimate the nuclear-to-cytoplasmic (N:C) ratio and produce a qualitative "atypia score." The authors propose a hybrid deep-learning and morphometric model that reliably automates the Paris System.Whole-slide images (WSI) of liquid-based urine cytology specimens were extracted from 51 negative, 60 atypical, 52 suspicious, and 54 positive cases. Morphometric algorithms were applied to decompose images to their component parts; and statistics, including the NC ratio, were tabulated using segmentation algorithms to create organized data structures, dubbed rich information matrices (RIMs). These RIM objects were enhanced using deep-learning algorithms to include qualitative measures. The augmented RIM objects were then used to reconstruct WSIs with filtering criteria and to generate pancellular statistical information.The described system was used to calculate the N:C ratio for all cells, generate object classifications (atypical urothelial cell, squamous cell, crystal, etc), filter the original WSI to remove unwanted objects, rearrange the WSI to an efficient, condensed-grid format, and generate pancellular statistics containing quantitative/qualitative data for every cell in a WSI. In addition to developing novel techniques for managing WSIs, a system capable of automatically tabulating the Paris System criteria also was generated.A hybrid deep-learning and morphometric algorithm was developed for the analysis of urine cytology specimens that could reliably automate the Paris System and provide many avenues for increasing the efficiency of digital screening for urine WSIs and other cytology preparations.
GILE: A Generalized Input-Label Embedding for Text Classification

Neural text classification models typically treat output labels as categorical variables that lack description and semantics. This forces their parametrization to be dependent on the label set size, and, hence, they are unable to scale to large label sets and generalize to unseen ones. Existing joint input-label text models overcome these issues by exploiting label descriptions, but they are unable to capture complex label relationships, have rigid parametrization, and their gains on unseen labels happen often at the expense of weak performance on the labels seen during training. In this paper, we propose a new input-label model that generalizes over previous such models, addresses their limitations, and does not compromise performance on seen labels. The model consists of a joint nonlinear input-label embedding with controllable capacity and a joint-space-dependent classification unit that is trained with cross-entropy loss to optimize classification performance. We evaluate models on full-resource and low- or zero-resource text classification of multilingual news and biomedical text with a large label set. Our model outperforms monolingual and multilingual models that do not leverage label semantics and previous joint input-label space models in both scenarios.
The chequered past and risky future of digital forensics

There is weak integration of digital forensics and forensic science, despite over a decade of effort to break down the borders between them. As more criminal investigations involve digital traces in increasing amounts and complexity, the quality of digital forensic results is decreasing and comprehension of cybercrime is diminishing. The consequences of errors and omissions in digital forensics include imprisoning innocent people, leaving dangerous criminals free to perpetrate additional crimes, and continuing victimization of the organizations and people targeted by offences. To mitigate these risks it is necessary to harmonize digital forensics and forensic science, and to strengthen knowledge management throughout decentralized forensic ecosystems. To drive the needed harmonization and knowledge management, there are two essential roles: the investigative advisor with operational experience, and the forensic advisor with scientific background. These advisors can negotiate the borders between police, digital forensic specialists, criminal intelligence analysts and attorneys to cultivate a criminal justice system that treats digital traces effectively, has visibility across criminal activities, and addresses crime and security more strategically.
Convolutional Neural Network Based Text Steganalysis

The prevailing text steganalysis methods detect steganographic communication by extracting hand-crafted features and classifying them using SVM. However, these features are designed based on the statistical changes caused by steganography, thus they are difficult to adapt to different kinds of embedding algorithms and the detection performance is heavily dependent on the text size. In this letter, we propose a novel text steganalysis model based on convolutional neural network, which is able to capture complex dependencies and learn feature representations automatically from the texts. First, we use a word embedding layer to extract the semantic and syntax feature of words. Second, the rectangular convolution kernels with different sizes are used to learn the sentence features. To further improve the performance, we present a decision strategy for detecting the long texts. Experimental results show that the proposed method can effectively detect different kinds of text steganographic algorithms and achieve comparable or superior performance for a wide variety of text sizes compared with the previous methods.
Persistent and Robust Execution of MAPF Schedules in Warehouses

Multi-agent path finding (MAPF) is a well-studied problem in artificial intelligence that can be solved quickly in practice when using simplified agent assumptions. However, real-world applications, such as warehouse automation, require physical robots to function over long time horizons without collisions. We present an execution framework that can use existing single-shot MAPF planners and ensures robust execution in the presence of unknown or time-varying higher-order dynamic limits, unforeseen robot slow-downs, and unpredictable obstacle appearances. Our framework also naturally enables the overlap of re-planning and execution for persistent operation and requires little communication between robots and the centralized planner. We demonstrate our approach in warehouse simulations and in a mixed reality experiment using differential drive robots. We believe that our solution closes the gap between recent research in the artificial intelligence community and real-world applications.
Discussing with a computer to practice a foreign language: research synthesis and conceptual framework of dialogue-based CALL

This article presents the results of a systematic review of the literature on dialogue-based CALL, resulting in a conceptual framework for research on the matter. Applications allowing a learner to have a conversation in a foreign language with a computer have been studied from various perspectives and under different names (dialogue systems, conversational agents, chatbots…). Considering the fragmentation of what we identify under the term dialogue-based CALL, we attempt to offer a structured overview of these efforts into a conceptual framework. Through a methodical search strategy, we collected a corpus of 343 publications. From this corpus, we formalized an operational definition of dialogue-based CALL, which allowed us to identify 96 relevant systems. Analyzing the type of dialogue they offer, on a continuum of constraints on form and meaning, we propose to classify those systems into four groups. We have called these branching, form-focused, goal-oriented and reactive systems, and we describe their corresponding interactional, instructional and technological traits. We summarize the main results from empirical studies on such systems, distinguishing observational, survey and experimental studies, and discuss the impact of dialogue-based CALL on motivation and L2 development, identifying positive evidence on both outcomes. Finally, we propose two main avenues for future research: relative effectiveness of dialogue-based CALL approaches, and dialogue systems as an environment for testing SLA hypotheses.
Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

Biofeatures are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summarise the factors that hinder biometrics models’ development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented. More specifically, we analyze the state of the art of the existing biometric-based authentication schemes for IoT devices. Based on the current taxonomy, we conclude our paper with different types of challenges for future research efforts in biometrics-based authentication schemes for IoT devices.
Determination of cloud transmittance for all sky imager based solar nowcasting

The demand for accurate solar irradiance nowcast increases together with the rapidly growing share of solar energy within our electricity grids. Intra-hour variabilities, mainly caused by clouds, have a significant impact on solar power plant dispatch and thus on electricity grids. All sky imager (ASI) based nowcasting systems, with a high temporal and spatial resolution, can provide irradiance nowcasts that can help to optimize CSP plant operation, solar power plant dispatch and grid operation. The radiative effect of clouds is highly variable and depends on micro- and macrophysical cloud properties. Frequently, nowcasting systems have to measure/estimate the radiative effect during complex multi-layer conditions with strong variations of the optical properties between individual clouds. We present a novel approach determining cloud transmittance from measurements or from correlations of transmittance with cloud height information. The cloud transmittance is measured by a pyrheliometer when shaded, as the ratio of shaded direct normal irradiance (DNI) and clear sky DNI. However, for most clouds, direct transmittance measurements are not available, as these clouds are not shading the used pyrheliometers. These clouds receive an estimated transmittance value based on (1) their height, (2) results of a probability analysis with historical cloud height and transmittance measurements as well as (3) recent transmittance measurements and their corresponding cloud height. Cloud heights are measured by a stereoscopic approach utilizing two ASIs. We discuss site dependencies of the presented transmittance estimation method and the potential integration of automatic cloud classification approaches. We validated the cloud transmittance estimation over two years (2016 and 2017) and compare the probabilistic cloud transmittance estimation approach with four simple approaches. The overall mean-absolute deviation (MAD) and root-mean-square deviation (RMSD) are 0.11 and 0.16 respectively for transmittance. The deviations are significantly lower for optically thick or thin clouds and larger for clouds with moderate transmittance between 0.18 and 0.585. Furthermore we validated the overall DNI forecast quality of the entire nowcasting system, using this transmittance estimation method, over the same data set with three spatially distributed pyrheliometers. Overall deviations of 13% and 21% are reached for the relative MAD and RMSD with a lead time of 10 min. The effects of the chosen data set on the validation results are demonstrated by means of the skill score.
The drivers of Bitcoin demand: A short and long-run analysis

Since 2010, Bitcoin has shown high price volatility, spurring a debate regarding the underlying reasons that lead economic agents to demand it. This paper analyzes the demand for Bitcoin in order to determine whether it stems from Bitcoin's utility as a medium of exchange, a speculative asset, or as a safe-haven commodity. We examine Bitcoin from a monetary-theory perspective and build a demand model that explores both the long-term and short-term relationships among variables. Our findings show that Bitcoin behaves as a speculative asset in the short term. In the long term, however, speculation does not seem to influence demand for Bitcoin. Instead, demand might be driven by expectations regarding Bitcoin's future utility as a medium of exchange.
New fuzzy C-means clustering method based on feature-weight and cluster-weight learning

Among fuzzy clustering methods, fuzzy c-means (FCM) is the most recognized algorithm. In this algorithm, it is assumed that all the features are of equal importance. In real applications, however, the importance of the features are different and there exist some features that are more important than the others. These important features should basically have more effects than the other features in the forming of optimal clusters. The basic FCM algorithm does not support this idea. Also, the FCM algorithm suffers from another problem; the algorithm is very sensitive to initialization, whereas a bad initialization leads to a poor local optima. Some improved versions of FCM have been proposed in the literature, each of which has somehow mitigated the first problem or the second one. In this paper, motivated by these weaknesses of the FCM, the goal is to solve the two problems at the same time. In doing so, an automatic local feature weighting scheme is proposed to properly weight the features of each clusters. And, a cluster weighting process is performed to mitigate the initialization sensitivity of the FCM. Feature weighting and cluster weighting are performed simultaneously and automatically during the clustering process resulting in high quality clusters, regardless of the initial centers. Extensive experiments conducted on a synthetic dataset and 16 real world datasets indicate that the proposed algorithm outperforms the state-of-the-arts algorithms. The convergence proof of the proposed algorithm is also provided.
A survey of self-admitted technical debt

Technical Debt is a metaphor used to express sub-optimal source code implementations that are introduced for short-term benefits that often need to be paid back later, at an increased cost. In recent years, various empirical studies have focused on investigating source code comments that indicate Technical Debt often referred to as Self-Admitted Technical Debt (SATD). Since the introduction of SATD as a concept, an increasing number of studies have examined various aspects pertaining to SATD. Therefore, in this paper we survey research work on SATD, analyzing the characteristics of current approaches and techniques for SATD detection, comprehension, and repayment. To motivate the submission of novel and improved work, we compile tools, resources, and data sets made available to replicate or extend current SATD research. To set the stage for future work, we identify open challenges in the study of SATD, areas that are missing investigation, and discuss potential future research avenues.
Post-Quantum Cryptoprocessors Optimized for Edge and Resource-Constrained Devices in IoT

By exponential increase in applications of the Internet of Things (IoT), such as smart ecosystems or e-health, more security threats have been introduced. In order to resist known attacks for IoT networks, multiple security protocols must be established among nodes. Thus, IoT devices are required to execute various cryptographic operations, such as public key encryption/decryption. However, classic public key cryptosystems, such as Rivest-Shammir-Adlemon and elliptic curve cryptography are computationally more complex to be efficiently implemented on IoT devices and are vulnerable regarding quantum attacks. Therefore, after complete development of quantum computing, these cryptosystems will not be secure and practical. In this paper, we propose InvRBLWE, an optimized variant for binary learning with errors over the ring (Ring-LWE) scheme that is proven to be secure against quantum attacks and is highly efficient for hardware implementations. We propose two architectures for InvRBLWE: 1) a high-speed architecture targeting edge and powerful IoT devices and 2) an ultralightweight architecture, which can be implemented on resource-constrained nodes in IoT. The proposed architectures are scalable regarding security levels and we provide experimental results for two versions of the InvRBLWE scheme providing 84 and 190 bits of classic security. Our implementation results on field programmable gate array dominate the best of the classic and post-quantum previous implementations. Moreover, our two different application specific integrated circuit (ASIC) implementations show improvement in terms of speed, area, power, and/or energy. To the best of our knowledge, we are the first to implement learning with error-based cryptosystems on ASIC platform.
Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science

Scientific research of all kinds should be guided by statistical thinking: in the design and conduct of the study, in the disciplined exploration and enlightened display of the data, and to avoid statistical pitfalls in the interpretation of the results. However, formal, probability-based statistical inference should play no role in most scientific research, which is inherently exploratory, requiring flexible methods of analysis that inherently risk overfitting. The nature of exploratory work is that data are used to help guide model choice, and under these circumstances, uncertainty cannot be precisely quantified, because of the inevitable model selection bias that results. To be valid, statistical inference should be restricted to situations where the study design and analysis plan are specified prior to data collection. Exploratory data analysis provides the flexibility needed for most other situations, including statistical methods that are regularized, robust, or nonparametric. Of course, no individual statistical analysis should be considered sufficient to establish scientific validity: research requires many sets of data along many lines of evidence, with a watchfulness for systematic error. Replicating and predicting findings in new data and new settings is a stronger way of validating claims than blessing results from an isolated study with statistical inferences.
Research on Network Intrusion Detection Based on Incremental Extreme Learning Machine and Adaptive Principal Component Analysis

Recently, network attacks launched by malicious attackers have seriously affected modern life and enterprise production, and these network attack samples have the characteristic of type imbalance, which undoubtedly increases the difficulty of intrusion detection. In response to this problem, it would naturally be very meaningful to design an intrusion detection system (IDS) to effectively and quickly identify and detect malicious behaviors. In our work, we have proposed a method for an IDS-combined incremental extreme learning machine (I-ELM) with an adaptive principal component (A-PCA). In this method, the relevant features of network traffic are adaptively selected, where the best detection accuracy can then be obtained by I-ELM. We have used the NSL-KDD standard dataset and UNSW-NB15 standard dataset to evaluate the performance of our proposed method. Through analysis of the experimental results, we can see that our proposed method has better computation capacity, stronger generalization ability, and higher accuracy.