Reviewerly/exHarmony huggingface dataset:
Effect of Maximizing Recall and Agglomeration of Feedback on Accuracy

This paper studies multiple aspects of modeling user preference in a heterogeneous environment, where different individuals describe their level of comfort with the temperatures in different rooms in a building. The study shows that sampling based on fuzzy clustering provides the best approach for addressing the imbalance resulting from limited feedback points. Another issue addressed in the paper is a comparison of models based on agglomerated dataset for the entire building versus datasets for individual rooms. Ideally, personalized models for individual rooms should provide the best models. However, the number of feedback points for individual rooms is much smaller resulting in even larger imbalance in data. In many diagnostic situations in engineering and health sciences, recalling the critical decisions is more important than the prediction accuracy. The paper studies the quality of modeling by maximizing recall versus maximizing the AUC of models.
Knowledge Representation for Emotion Intelligence

Emotion intelligence (EI) is a traditional topic for psychology, sociology, biology and medical science. Because emotion is related with the personality, interpersonal effect, social function, disease treatment, etc. Analyzing the emotion from the Web data by computer technology becomes more and more popular, and the scientists from the non-computer domains need more helpful computing models to deal with professional problems that are not traditional for computer science. Knowledge representation is a basic and possible solution as a bridge between emotion intelligence and artificial intelligence. For the sentiment words, word embedding can map the words to vectors that represent the semantic context of the words. Sentiment embedding based on the word embedding can capture both semantics and the emotion information. We have introduced two kinds of improving embedding methods (MEC and Emo2Vec) for the sentiment words embedding. For emotion structure based on the psychology of emotion, knowledge graph can represent the cognitive relations between different emotion types. The same emotional expressions can affect the reaction and behaviors of the recipient in different ways due to factors such as social relations, information processing, time pressure, etc. Knowledge graph can represent these complicated situations as the relations between the entities and attributes. Based on this graph, we make the inference or prediction of the emotion influence on decision making.
Evaluation of Geological Risks of Commercial Development of Complex Structured Coal Deposits

The main risk in the commercial development of complex located coal deposits is a geological risk, which is assessed by the results of coal resources classification at sufficient confidence. In conditions of complex located coal deposits, the main factor which determines categories of coal Reserves is the confidence of geological structural data, a reliable estimation of which is provided only with the use of quantitative methods. The use of conventional geostatistical and geometrical approaches to the reserves categorization in this kind of deposits is impossible. The confidence of complex located coal deposits is proposed to be quantitatively evaluated by the similarity degree of seams position on the adjacent exploration lines. This similarity is estimated with a special similarity coefficient varying from 0 to 1, determined based on curves correlation, which is adapted to the problem-solving method, in the complex plane. The obtained results provide the quality increase of the geological materials confidence evaluation and can be used to solve issues of the geological risk evaluation of the development of complex located coal deposits, during commercial or state expertise of geological information.
PROBLEM OF UNITY OF RATIONAL AND SENSUAL IN DEVELOPMENT OF COGNITIVE ABILITIES

The problem of the unity of the rational and the sensorial, of the cognitive and affective processes in the human psyche has deep roots in prehistoric times, when the civilization originated in the form of the primitive society. That period is considered to be the basis, on which the historically original form of cognizance of the world was formed, i. e. the mythological and pagan worldview of the primitive people. In various forms of collective creativity, the syncretism of their thinking manifested itself that, in turn, testified to the undivided nature of their attitude to the surrounding world in its unity of intellectual, sensorial and volitional manifestations. With the advent of the naively spontaneous philosophical outlook characteristic of the philosophy of nature, the notion of «logos» appeared in the sense of the general law of being, the very foundation of the Universe and the human existence. Ancient Greek thinkers began the centuries-old Occidental tradition of exalting the reason over sensations. In medieval times, philosophical thinking depended on religion and, therefore, in the initial period of its development, the so-called patristic, the leading principle was the recognition of the primacy of faith over reason. At the same time, opposing views were also expressed about the identity of the above notions and their harmonious coexistence. During the era of the Renaissance, the role of the reason and logic was also exalted in contrast to the sensoria. The philosophy of the modern era led to the emergence of the two opposing schools of philosophical knowledge, i. e. empiricism and rationalism, whose representatives, respectively, showed the primacy of either sensory experience or reason in the process of cognition. In that period, the «logic of the heart» emerged that purported to be above both the sensoria and the reason. The heart was seen as a kind of an instrument of the faith endowed with the logic of its own. That period’s sensualist school of philosophy recognized sensations, sensual experience as the only source of knowledge. During the Enlightenment, the power of the human mind was asserting itself as the supreme ideal of the era, although some philosophical views did continue to espouse the «idea of the heart». In the self-same period, a tendency was observed to combine the advantages of rationalism and empiricism, the belief in the cognitive abilities of both the human mind and the human sensorial experience. In the German classical philosophy, with its dominant tendency towards the absolutization of the reason, there was also a parallel development of the tendencies towards the recognition of the priority of human sensations and the harmonization of the reason and sensoria. The whole set of epistemological problems of the modern philosophy is characterized by pluralism, the desire to synthesize the most fruitful ideas, theories, concepts, also including the idea of combining the opposite sides of the cognitive process, i. e. the rational and the sensorial.
Adaptive Collaborative Filtering for Recommender System

On online websites or e-commerce services, the explosive growth of resource makes the problem of content exploring increasingly challenging. The recommender system is a powerful information filtering tool to support user interaction and promote products. Dealing with determining customer interests, graph-based collaborative filtering is recently the most popular technique. Its only drawback is high computing cost, leads to bad scalability and infeasibility for large size network. Moreover, most previous studies concentrate solely on the accuracy of user preference prediction, while the efficiency of recommendation methods should be considered in many characteristics with complicated relationships, depending on particular systems: popularity, diversity, coverage, congestion. Attempt to conquer these challenges, we propose Adaptive Weighted Conduction formula handling multiple metrics, then construct a scalable model with small complexity, named Adaptive Collaborative Filtering. Experiments are conducted on Movielens, a public dataset, and FPT PLAY, a dataset of our media service. We have an increase of $$6\%$$ on precision and get close to the best of previous methods on diversity, coverage and congestion. This result shows that the proposed model automatically reveals and adapts to varied requirements of recommender systems, reaches high accuracy and still balances other evaluation aspects.
Supporting Collaborative Use of Self-Tracking Data in the Context of Healthcare and Chronic Conditions

Recent growth in the use of consumer self-tracking technologies has attracted attention from patients and healthcare providers alike, while researchers have begun to explore its value in the context of chronic health conditions. Sensemaking challenges faced by patients and clinicians have shifted the focus of self-tracking studies towards co-interpretation. Yet workflow constraints and conflicting interests remain barriers to the integration of self-tracking data into clinical conversations. My research aims to address these issues with designs of interactive systems that support the collaborative use of patient self-tracking data.
Knowledge Without Complete Certainty

We present an epistemic logic ELF (Epistemic Logic with Filters) where knowledge does not require complete certainty. In this logic, instead of saying that an agent knows a particular fact if it is true in every accessible world, we say that it knows the fact if it is true in a sufficiently large set accessible worlds. On a technical level, we do this by enriching the standard Kripke models of epistemic logic with a set of filters: a sufficiently large set of worlds is one that is in the filter. We introduce semantics for ELF, and give a sound and complete proof system.
Study on the Countermeasures of E-government Cloud Security Supervision in Shandong Province

Regarding the cloud security supervision of e-government in Shandong Province, although the supervision units have now established a hierarchical protection testing mechanism and related technical means, there is no effective targeted technical means for security monitoring of important websites and important information systems, which cannot fully grasp the system security situation, clearly and accurately judge the existence of security risks, at the same time, there is no active monitoring and prior detection of loopholes of the ability and means. Based on the development of e-government cloud in Shandong Province, this paper analyzes the trend of e-government cloud security supervision in Shandong Province, proposes a feasible e-government cloud security supervision system, clarifies the security responsibility of cloud service providers, users and managers, controls security risks, and guarantees the security of government affairs information system to go to the cloud.
Study on the Construction Mode of Ecological and Sustainable Rain Flood in New Urban Area of China

In the background of the urbanization process has exceeded 58% of China and expansion of the construction land expansion, as a future appropriate residential development and ahead of the key area of urban rain flood management, this research expounds the new cities and new district how to follow the principle of ecological priority in different application field, and how to use land for urban green space and ecological protection for elastic development and rain flood management. The study of the new cities’ eco-rain flood management model is of great significance for the governance and sustainable development of urban diseases in China.By combining natural ways with artificial measures, planning is carried out in advance.On the premise of ensuring the safety of urban drainage and waterlogging prevention, the accumulation, infiltration and purification of rainwater in urban areas can be realized to the maximum extent, and the utilization of rainwater resources and ecological environment protection can be promoted in new cities. To make plan ahead,to avoid the problems of rain and flood in existing urban areas, and improve the livable quality of new cities greatly.New ways of thinking and construction guidance are proposed for ecological livable construction and flexible rain flood management of new urban areas in China.
LeMo: Protecting Kernel with Least Privilege Modules

The Linux kernel is the monolithic kernel, and the boundaries among the objects in the kernel are not particularly clear. Once the malicious module is loaded in the kernel, it can almost access the entire kernel. This breaks the principle of least privilege. To overcome it, in this paper, we propose LeMo which is the novel architecture to hold the module with least privilege in the kernel. In LeMo, the modules are restricted to access the necessary kernel objects. To the end, before the module is loaded in the kernel, the patched kernel build a new page table for the module. With page-based access control, the patched kernel is capable of preventing the malicious modules to arbitrarily access the kernel. We have implemented the prototype of LeMo which provide the tools which load or unload the module. Our evaluation show that LeMo is able to defeat the malicious module with a acceptable performance overhead.
Paraphrase Detection using Dependency Tree Recursive Autoencoder

An architecture is proposed based on recursive autoencoder for paraphrase detection. The proposed architecture embeds the semantic information by using word representations generated from the neural network language model and the syntactic information by implementing the dependency tree over the recursive autoencoder, where dependency tree reveals the syntactic information of the given sentence in recursive form. The proposed architecture is tested on the MSRP dataset for paraphrase detection and the results are above the baseline. The proposed system reached a moderate accuracy and F1 score for the paraphrase detection test on MSRP dataset.
Enhancing Rating Prediction by Discovering and Incorporating Hidden User Associations and Behaviors

Collaborative filtering (CF)-based rating prediction would greatly benefit by incorporating additional user associations and behavioral similarity. This article focuses on infusing such additional side information in three common techniques used for building CF-based systems. First, multi-view clustering is used over neighborhood-based rating predictions. Secondly, additional user behavior knowledge discovered by mining user reviews are infused into non-negative matrix factorization (NMF) techniques. Finally, the article explores how to infuse such additional behavioral knowledge into a Deep Neural Network (DNN) based DF architecture. The article also explores using term frequency-inverse document frequency (TF-IDF) vectors as the input to DNN. Since TF-IDF does not directly capture the conceptual contents of the text or the behavioral aspects of the writer, the article also proposes a novel scheme called topic proportions-inverse entity frequency (TP-IEF) that uses topics discovered from reviews instead of words to better capture semantic associations between users and items.
A Novel Neural Collaborative Filtering Model with Auxiliary Information

In recent years, deep learning has been widely used in personalized recommendation systems. Utilizing deep learning to model user-item complex interaction function is a trend of current recommendation domain. However, most of them only consider the rating information of users and items, ignoring the effect of auxiliary information on the interaction function. In this work, we propose a novel neural network architecture named ANCF, which makes full use of rating and auxiliary information. In contrast to existing deep learning based recommender models that use SDAEs to extract high-level representation of one-hot encoded auxiliary information, ANCF utilizes embedding vector to represent auxiliary information and non-linear neural networks, including attention networks and deep interaction layers, to model user-item interaction function. Extensive experiments on three real-world datasets demonstrate the effectiveness of our proposed ANCF framework.
Ital. eravassimo e altre forme verbali in -vassimo

Abstract This article aims to reconstruct the history of eravassimo (first-person plural of the imperfect indicative of essere ) and other Italian verbal forms ending in - vassimo . These rare verbal forms began to appear in grammars and dictionaries in the 17th century and in some literary and non-literary texts in the 18th century. Some examples of the verbal forms in - vassimo can still be found in texts produced in recent years on the Internet.
Business model of blockchain enabled smart city cervices

Abstract. This thesis is qualitative research analyzing how blockchain can influence the business models that are for smart city services. Blockchain technology is seen as an innovation that will change the functioning of city services in the future. The study designed to see the effect of the business model on implementation of blockchain enabled smart city services. This study combines the Causal Layered Analysis and Business Model Wheel to see the influence. In doing so, this study chose to understand the futures of a tech-aided social context; the blockchain enabled smart city services.The theoretical part of this study consists of smart city services, fundamental of blockchain technology, and business model. The literature review enabled the author to collect the data efficiently. The empirical part is based on the semi-structured interviews from the professionals from city organization and researchers on the research topic area. The author provided a discussion on different business model elements for the blockchain enabled smart city sector in Finland.This research contributes to a holistic understanding of the business model, smart city potentials and applies blockchain in a different layer of smart city services. This paper looks at the future scenarios of the business model and discusses how blockchain can make an impact. This research also combines the foresight of future city structures and business modeling at future scenarios.This research will also help the researchers, city managers, business development professional for understanding future cities and how innovative technology make an impact on city business model. This comprehensive study also helps future business perspective for city associated organization. The outcome of the research can be used for identifying a business opportunity and to understand the influence of technology on the business model.This study uncovers some business model elements and its effect on short, mid, and long term future. All in all, with a solid theoretical base, high-quality empirical dataset, well-argued results, and maintaining ethical standards, this study offers a comprehensive description of the studied topic.
Enhancing Image Steganalysis with Adversarially Generated Examples

The goal of image steganalysis is to counter steganography algorithms which attempt to hide a secret message within an image file. We focus specifically on blind image steganalysis in the spatial domain which involves detecting the presence of secret messages in image files without knowing the exact algorithm used to embed them. In this paper, we demonstrate that we can achieve better performance on the blind steganalysis task by training the YeNet architecture with adversarially generated examples provided by SteganoGAN.
High-fidelity Symbol Synchronization for Specific Emitter Identification

Specific emitter identification (SEI) provides the capability to distinguish radio emitters with the external features carried by the received waveforms. Since the differences between emitters are very subtle, the fidelity of signal processing may affect the identification accuracy. In this paper, we propose a high-fidelity symbol synchronization approach to depress the processing errors introduced by demodulation. First, two existing problems of timing recovery are pointed out in SEI scenario. Thereafter, we present a window-based interpolator and a feedforward decision-directed timing estimator to address these problems. Simulation results show that our approach effectively depresses the processing errors compared with the existing methods. Furthermore, it achieves a better performance of SEI.
Scene disparity estimation with convolutional neural networks

Estimation of stereovision disparity maps is important for many applications that require information about objects’ position and geometry. For example, as depth surrogate, disparity maps are essential for objects’ 3D shape reconstruction and indeed other applications that do require three-dimensional representation of a scene. Recently, deep learning (DL) methodology has enabled novel approaches for the disparity estimation with some focus on the real-time processing requirement that is critical for applications in robotics and autonomous navigation. Previously, that constraint was not always addressed. Furthermore, for robust disparity estimation the occlusion effects should be explicitly modelled. In the described method, the effective detection of occlusion regions is achieved through disparity estimation in both, forward and backward correspondence model with two matching deep subnetworks. These two subnetworks are trained jointly in a single training process. Initially the subnetworks are trained using simulated data with the know ground truth, then to improve generalisation properties the whole model is fine-tuned in an unsupervised fashion on real data. During the unsupervised training, the model is equipped with bilinear interpolation warping function to directly measure quality of the correspondence with the disparity maps estimated for both the left and right image. During this phase forward-backward consistency constraint loss function is also applied to regularise the disparity estimators for non-occluding pixels. The described network model computes, at the same time, the forward and backward disparity maps as well as corresponding occlusion masks. It showed improved results on simulated and real images with occluded objects, when compared with the results obtained without using the forward-backward consistency constraint loss function.
A STUDY ON ROLE OF INTRA COMMUNICATION ON EMPLOYEE ENGAGEMENT IN BSNL, HYDERABAD TELECOM DISTRICT

Employee engagement could be a gently one of the new phenomenon that continues to gather the importance and implementation into organizations. Whereas Intra Communication is also additionally known by experts and management as the factor which is to be addressed immediately an important tool for employee engagement.  In Most of the organizations, communicating effectively with their employees creates an environment where employees seem to believe in the organization's goals and thus exhibit a lot of effort throughout their workday. However, till now less scientific researchers has concentrated on the relationship between intra communication and employee engagement.  Taking this into account, present research paper examined “whether there is a relationship between intra communication and employee engagement” or not. The study was targeted on identifying the vital drivers that have an effect on the level of employee engagement in BSNL (Bharat Sanchar Nigam Limited), Hyderabad Telecom District. Research was more confined to find out impact of intra communication on the three major factors of employee engagement “Commitment”, “Meaningfulness of their (employees) work” and ‘Discretionary effort”.