Miracle-dz/UniFAR-FTUs huggingface dataset:
CCSMR: A Combinatorial Category Space-Based Model for Recommendation

Various side information has been exploited in recommender systems to help users finding items they prefer to alleviate data sparsity. Because item category can be used to view the user’s preference in a high-level scope and an item can have more than one associated category, the existing works leverage this characteristic, that enriches the information density of item, to further improve the recommendation performance. However, the existing works either treat item categories or their correlations as additional information to compute the similarity between users and items, or introduce category hierarchy as a latent factor into matrix factorization (MF) models. In these works, each item corresponds to a category set, that makes it too difficult to utilize item category’s potential and valuable information. In this paper, we propose a novel combinatorial category space based recommendation model, which can fully exploit item category information and reduce the difficulty by converting the relationship between item and its categories from one-to-many to one-to-one. It considers a combinatorial category associated with an item, not each of them individually. Specifically, we first define a combinatorial category space (CCS), which is viewed as a subset-superset lattice of item categories. Then semantic relations and semantic distances are defined to model a user preference, and a compound graph (CoGraph) is constructed to model users’ interactive behaviors in CCS, respectively. Finally, two recommendation models, CCSRank and CCSMF, are exploited on user interactive graph. We carry out extensive experimental evaluations on two real datasets to show the effectiveness of our model.
Low-Complexity Hybrid Precoding for Multi-User MmWave Systems With Low-Resolution Phase Shifters

Large-scale antennas systems have been attracted significant attention to provide considerable array gains for millimeter wave (mmWave) communications systems. However, huge hardware complexity and power consumptions make the conventional precoding architectures impractical to realize. Recently, hybrid precoding structures for mmWave communications systems are widely investigated to save the cost and achieve satisfactory performance. Unfortunately, most existing precoder designs lie in the assumption of infinite or high-resolution phase shifters (PSs), which are also difficult to realize in real-world. Motivated by this fact, a low complexity hybrid precoder and combiner design for multi-user systems with two-bit resolution PSs is proposed in this paper, which provides an appropriate trade-off between energy efficiency and system sum-rate. Specifically, the candidate sets for analog beamforming are constructed by introducing the auxiliary variables, in which the number of elements is the same as that of antennas. Accordingly, the computational complexity of searching optimal beamformer is significantly reduced over proposed codebooks. Then, to avoid high complexity singular value decomposition (SVD) of channel matrix, we propose a rank-1 approximate decomposition algorithm based-on principal component analysis technique. At last, the user scheduling algorithm is presented to further improve the performance in multiuser-scenario. Simulation results verified the effectiveness of the proposed algorithms, the performance of which is close to the optimal unquantized precoder.
Monotonicity Dataset Creation on Crowdsourcing

evaluating neural models on recognizing textual entailment (RTE). However, it is still unclear whether neural models can capture logical inferences, including monotonicity reasoning, for which no large naturalistic dataset has yet been developed. To investigate this issue, we introduce a method of creating a dataset for monotonicity reasoning by crowdsourcing and report the result of the ﬁrst run. The error analysis indicates that workers tend to provide diﬀerent answers from what logical entailment deﬁnes, for some downward monotonicity reasonings involving pragmatic reasoning.
How Consumer Technology Is Changing the IT Function: A Multi-Case Study of Three Fortune 500 Companies

ABSTRACT This study investigates how three different IT departments responded to IT consumerization. Using dynamic capabilities theory, we classified IT departments’ responses as sensing, seizing or transforming. This research shows how IT consumerization impacts each IT department’s deep structure and how the resulting changes can move beyond the scope of consumer tools and impact IT department operations. We demonstrate how consumerization, while creating struggles, creates shared governance, power redistribution and innovation for the IT workforce.
Chatbots Explain Themselves: Designers' Strategies for Conveying Chatbot Features to Users

Recently, text-based chatbots had a rise in popularity, possibly due to new APIs for online social networks and messenger services, and development platforms that help dealing with all the necessary Natural Language Processing. But, as chatbots use natural language as interface, their users may struggle to discover which sentences the chatbots will understand and what they can do. Because of that it is important to support their designers in deciding how to convey the chatbots’ features, as this might determine whether the user will continue chatting or not. In this work, our goal is to analyze the communicative strategies used by popular chatbots when conveying their features to users. We used the Semiotic Inspection Method (SIM) for that end, and as a result we were able to identify a series of strategies used by the analyzed chatbots for conveying their features to users. We then consolidate these findings by analyzing other chatbots. Finally, we discuss the use of these strategies, as well as challenges for designing such interfaces and limitations of using SIM on them.
Does activation really spread

In this article we consider three versions of the spreading activation model for retrieval of information from memory. Predictions are derived for activation spreading down a linear network structure as a function of time. To test these predictions, two experiments were performed in which facilitation as a function of time was measured for target words in a linearly structured paragraph. A target word was primed either by a word near to the target in the paragraph structure or by a word far from the target. Results showed that facilitation begins at about the same time for the far and near conditions. This is inconsistent with the predictions derived from the models. Implications of these results are discussed and alternative conceptions of the activation process are described. In this article we discuss the theoretical and empirical status of the spreading activation process. It is important to investigate this process, because it forms the central mechanism of two current models of memory—a model proposed by Anderson (1976) in the areas of memory, language, and thought, and a model proposed by Collins and Loftus (1975) in the area of semantic memory—and because it is becoming accepted as a mechanism for processes involved in text comprehension (Kieras, 1981; Miller, 1981). Spreading activation has also formed the basis of models in artificial intelligence (Fahlman, 1981; Hinton, 1981; Quillian, 1967), problem solving (Levin, 1976), language understanding (McDonald & Hayes-Roth, 1978), word recognition (McClelland & Rumelhart, 1981), and word production (Dell, 1980; Dell & Reich, 1977). Although we are concerned in this article with spreading activation as a process of retrieving information from memory, and therefore are most directly concerned with the models of Anderson and Collins and Loftus, the discussion presented here should also have implications for the other areas of research.
Extended Kalman Filter In Recurrent Neural Network: USDIDR Forecasting Case Study

Artificial Neural Networks (ANN) especially Recurrent Neural Network (RNN) have been widely used to predict currency exchange rates. The learning algorithm that is commonly used in ANN is Stochastic Gradient Descent (SGD). One of the advantages of SGD is that the computational time needed is relatively short. But SGD also has weaknesses, including SGD requiring several hyperparameters such as the regularization parameter. Besides that SGD relatively requires a lot of epoch to reach convergence. Extended Kalman Filter (EKF) as a learning algorithm on RNN is used to replace SGD with the hope of a better level of accuracy and convergence rate. This study uses IDR / USD exchange rate data from 31 August 2015 to 29 August 2018 with 70% data as training data and 30% data as test data. This research shows that RNN-EKF produces better convergent speeds and better accuracy compared to RNN-SGD.
Dynamic Balance and Gait Metrics for Robotic Bipeds

For legged robots to be useful in the real world, they must be able to balance and walk reliably. Both of these abilities improve when a system is more effective at moving itself around relative to its contacts (i.e., its feet). Achieving this type of movement depends both on the controller used to perform the motion and the physical properties of the system. Although much work has been done on the development of dynamic controllers for balance and gait, only limited research exists on how to quantify a system’s physical balance capabilities or how to modify the system to improve those capabilities. From the control perspective, there are three strategies for maintaining balance in bipeds: flexing, leaning, and stepping. Both stepping and leaning strategies typically depend on balance points (critical points used for maintaining or regaining balance) to determine whether or not a step is needed, and if so, where to step. Although several balance point estimators exist, the majority of these methods make undesirable assumptions (e.g., ignoring the impact dynamics, assuming massless legs, planar motion, etc.). From the physical design perspective, one promising approach for analyzing system performance is a set of dynamic ratios called velocity and momentum gains, which are dependent only on the (scale-invariant) dynamic parameters and instantaneous configuration of a system, enabling entire classes of mechanisms to be analyzed at the same time. This thesis makes four key contributions towards improving biped balancing capabilities. First, a dynamic bipedal controller is proposed which uses a 3D balance point estimator both to respond to disturbances and produce reliable stepping. Second, a novel balance point estimator is proposed that facilitates stepping while combining and expanding the features of existing 2D and 3D estimators to produce a generalized 3D formulation. Third, the momentum gain formulation is extended to general 2D and 3D systems, then both gains are compared to centroidal momentum via a spatial formulation and incorporated into a generalized gain definition. Finally, the gains are used as a metric in an optimization framework to design parameterized balancing mechanisms within a given configuration space. Effectively, this enables an optimization of how well a system could balance without the need to pre-specify or co-generate controllers and/or trajectories. To validate the control contributions, simulated bipeds are subjected to external disturbances while standing still and walking. For the gain contributions, the framework is used to compare gain-optimized mechanisms to those based on the cost of transport metric. Through the combination of gain-based physical design optimization and the use of predictive, real-time balance point estimators within dynamic controllers, bipeds and other legged systems will soon be able to achieve reliable balance and gait in the real world.
A Power Optimized Method for Mode Switching in Android Systems

How the Suspend/Resume mechanism of smartphone influences the power consumption is examined in the dissertation. Specifically, various unimportant and not so urgent network packets keep awakening the operating system (OS) at the time it is under suspend mode, and switch it from suspend to resume mode continually, which results in more power consumption. Accordingly, an innovative optimization technique was suggested in this paper in order that the awakening of OS can be postponed and the lasting hour of suspend mode can be lengthened to decrease power consumption. Some experiments are also carried out, with the result data suggesting that such technique is an effective way to reduce power consumption by greater than 7.63%. It proves that this technique is workable. Received on 08 May 2019; accepted on 26 July 2019; published on 06 August 2019
Autonomous Information Behaviour: Towards a Conceptual Model

Autonomous information behaviour (AIB) proposes that autonomous intelligent agents exhibit information behaviour characteristics (i.e. “need, seek, manage, give, and use information in different contexts” Fisher, Erdelez, & McKechnie, 2005, p. xix) similar to that in humans. A preliminary conceptual model of AIB, derived from Wilson (2016) and Domingos (2015), is presented. Understanding autonomous information behaviour is important given the increasing prevalence of algorithmic decision-making in everyday life.
Alternative approaches for analyses of production performance from automatic milking systems in South Africa

Globally, dairy producers are employing precision farming practices and incorporating computer software that enables producers to manage large herds at an individual animal level. South African dairy producers have been adopting similar strategies with a trend towards larger production units and the incorporation of automatic milking systems. The software employed in automatic systems record production, reproduction and health parameters on a daily basis. These systems record all variables and movements of individual animals, from the day of birth to the day the cow exits the herd. The majority of producers using automatic milking systems do not participate in national recording. The aim of this study was to perform a production analysis with the primary objective of constructing a template for extracting and analysing herd performance data from producers employing automatic management software in South Africa. Two large dairy herds, representing a TMR system, and a pasture-based production system participated in the study. Producers installed the AfiFarm herd management software from S.A. E Afikim, Kibbutz, Israel. By extracting animal records from multiple years, comprehensive data tables were constructed for different production analyses. Analyses included time-trend evaluation of herd numbers, mean production and reproduction performance at the heifer and cow level, distribution of exit reasons and assessing the relationship between the genetic merit of sires and the mean performance of their progeny. Findings from this study confirm that the AfiFarm herd management software permit extraction and analyses of multiple variables imperative to dairy management at the herd and cow level. The software has the potential to serve as a platform to add a vast number of dairy cow performance records for future analyses.
Perform the Magic! Usability testing for Magika, a Multisensory Environment fostering children's well being

This paper describes the result of an Usability Test over teachers’ control interface in Magika, the powering engine of the Magic Room, a low-cost MultiSensory Environment (MSE) that enables new forms of playful learning for children, especially those with special needs. Magika offers a set of activities integrated in a synesthetic way; it incorporates digital worlds projected on the wall and on the floor with a large number of connected objects, such as toys and materials, that allow children to experience tactile, auditory, visual, and olfactory stimuli. The MSE has been installed in two elementary schools, therefore it is an essential requirement that the system can be controlled completely autonomously by caregivers. A preliminary research, based on teacher’s needs, enlightened some basic requirements and allowed us to design a System Control Interface that enabled them to manage the experience within the MSE. The goals of testing the interface usability include: define a baseline of user performance, establish and validate user performance measures, and identify potential design concerns to be addressed in order to improve the efficiency, productivity, and end-user satisfaction. In this work, we explain in detail the administration of the experimental protocol to ten teachers, tested for half an hour each, of two main scenarios: the Create modality, where users explored how to configure activities and the Play modality, where they had to manage the room. Thanks to a background agent script that tracked number, position and time of each tap and results collected from SUS (System Usability Scale) questionnaire, we were able to obtain both quantitative and qualitative data useful to possibly improve our work and precisely evaluate the control Interface usability. Results appear to be consistent with the Usability of the control interface, as long as, no result was inferior of a score of 68 at SUS.
Feature Fusion: H-ELM based Learned Features and Hand-Crafted Features for Human Activity Recognition

Recognizing human activities is one of the main goals of human-centered intelligent systems. Smartphone sensors produce a continuous sequence of observations. These observations are noisy, unstructured and high dimensional. Therefore, efficient features have to be extracted in order to perform an accurate classification. This paper proposes a combination of Hierarchical and kernel Extreme Learning Machine (HK-ELM) methods to learn features and map them to specific classes in a short time. Moreover, a feature fusion approach is proposed to combine H-ELM based learned features with hand-crafted ones. Our proposed method was found to outperform state-of-the-art in terms of accuracy and training time. It gives an accuracy of 97.62% and takes 3.4 seconds as a training time by using a normal Central Processing Unit (CPU).
Microsystem Identification and Fingerprinting Using RF Side Channels

We have developed a comprehensive cyber sensor system for fingerprinting many classes of embedded devices, their internal hardware settings, and elements of their code. This is done by detecting changes to programmable embedded devices which impart unique signatures in their involuntary radio frequency (RF) emissions. In this paper, we show results of our approach for the following: i) device identification, ii) peripheral connection status , iii) compiler options used, and iv) data processing block placement. This sensor system is based on innovations in signal processing, code structure analysis, and machine learning, which in combination make it efficient, scalable, robust, and deployable in a wide variety of situations. Although results shown here cover only two classes of hardware (Arduino Uno IoT devices and Xilinx Artix 7 FPGAs) these methods naturally extend to many other types of hardware. There are a variety of use cases for this technology. It provides a robust fingerprinting mechanism for embedded devices and protection against counterfeits or unauthorized modifications. It can be effectively used for the verification of devices that have been already installed and for verifying supply-chain integrity. As this new sensor is not connected to the devices under test this technology is also safe from detection and manipulation by adversaries. Keywords—fingerprinting, vulnerability detection, cyber sensors, analog side-channel, RF emissions, signal processing, machine learning
Engineering Knowledge Graph for Keyword Discovery in Patent Search

Patent retrieval and analytics have become common tasks in engineering design and innovation. Keyword-based search is the most common method and the core of integrative methods for patent retrieval. Often searchers intuitively choose keywords according to their knowledge on the search interest which may limit the coverage of the retrieval. Although one can identify additional keywords via reading patent texts from prior searches to refine the query terms heuristically, the process is tedious, time-consuming, and prone to human errors. In this paper, we propose a method to automate and augment the heuristic and iterative keyword discovery process. Specifically, we train a semantic engineering knowledge graph on the full patent database using natural language processing and semantic analysis, and use it as the basis to retrieve and rank the keywords contained in the retrieved patents. On this basis, searchers do not need to read patent texts but just select among the recommended keywords to expand their queries. The proposed method improves the completeness of the search keyword set and reduces the human effort for the same task.
MACHINE LEARNING BASED CROP CYCLE MAPPING USING MULTI TEMPORAL SPACE DATASETS

Abstract. The Indo-Gangetic region is an important source of food grain produced in India. With advent of advanced crop management practices, there has been a recent increase in production. This could be due to improved yield and/or multiple crops per year. Understanding cropping patterns is, therefore, a fundamental requirement to strategize food security in the country. The long running space based earth observation programmes, provides a powerful tool to study the change in vegetation, through use of indices, synoptically and temporally. This study proposes a machine learning based technique to identify distributed (pixel-wise) cropping cycles, based on the temporal trend in normalized difference vegetation index (NDVI). The technique is applied to several time series NDVI data sets (OCM, MODIS and GIMMS) and results are discussed. There is tremendous scope to further extend this study to predict drought conditions, change in cropping pattern, and estimate production statistics, by using advanced artificial intelligence methods (such as deep learning).
Offline Inductance Identification of IPMSM With Sequence-Pulse Injection

Identification of the stator d-axis and q-axis inductances is essential for the interior permanent magnet synchronous motor (IPMSM) sensorless control system. The signal injection method is an effective way to identify the inductance parameters; however, it requires the rotor position to ensure the accuracy of the injection reference position. To overcome the dependence on the rotor position, a sequence-pulse voltage signal injection method is presented. The matrix least-squares fitting algorithm is utilized to fit the current responses for the inductance identification. Based on the fitting method, the discrete standard orthogonal polynomial with look-up table is introduced to reduce the computation complexity. To reduce the disturbance of the injected signal to the rotor position during the identification process, a double-direction injection position trajectory planning is presented. In addition, the saturation effect on the identification method is also analyzed. The proposed method is verified via a 3.0 kW IPMSM motor drive platform. The identification results are compared with those measured by a standard method and experiment results show that the proposed method can identify the inductance parameters with acceptable accuracy without obtaining the rotor position information.
Search for New Physics through the Reconstruction of Challenging Signatures with the ATLAS detector

Many theories of beyond the Standard Model (BSM) physics predict unique signatures which are difficult to reconstruct and the background rates are also a challenge. Signatures from displaced vertices anywhere from the inner detector to the muon spectrometer as well as those of new particles with fractional or multiple value of the charge of the electron or high mass stable charged particles are experimentally demanding signatures. The results of searches using data collected by the ATLAS detector in pp collisions at √ s = 13 TeV are presented