Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification algorithm based on semantics and text feature weighting

Text categorization faces the problems of dimensionality curse,noise data and different classification contributions for different feature words.In order to improve text classification accuracy,this paper presented a new approach to data processing.The approach first removed the noise data,and then employed feature extraction algorithms and semantic analysis methods to implement dimensionality reduction.Different weights were assigned to different text features based on a semantic similarity evaluation.The processed data were used to construct classifiers.Experimental results show that the text processing method can effectively improve the accuracy of text classification.
PREFABRICATION AS A SOURCE FOR CO-CREATION : AN INVESTIGATION INTO THE POTENTIALS FOR LARGE-SCALE PREFABRICATION IN THE UK

Prefabrication, as a means of customer co-creation, can offer great opportunities if perceived accordingly. The research evaluates the use of prefabrication within the hospitality sector in the UK and identifies the advantages of, and hindrances on, its application as a tool to encourage client participation in the design process. A mixed methodology has been used to gauge the general attitudes towards prefabrication, prevailing technical understanding and knowledge of the concept, its applicability, and its perceived advantages and disadvantages. The research indicates decreased construction times and increased quality as the main advantages and poor current education within the industry regarding prefabricated practices, the archaic nature of the industry lacking trust, reluctance to embrace new and innovative techniques, and the absence of a proven holistic and encompassing evaluation technique to provide accurate and reliable comparisons between differing construction methods as main obstacles on the way . The study facilitates the identification of key factors associated with stimulating more efficient use of prefabrication within the UK construction industry. The outcomes will help the industry to develop and evolve to suit the needs of, and overcome the constraints imposed by and on, today’s clients towards a more customer-centred construction industry.
Meaning in Life and Personality Traits in Adult Alcoholics

Correspondence to: Jelena Bupić, Department of Psychiatry, General Hospital Dubrovnik, Dr. Roka Mišetića 2, 20000 Dubrovnik, Croatia E-mail: bupic.jelena@gmail.com Copyright © 2019 KBCSM, Zagreb e-mail: alcoholism.kbcsm@gmail.com • www.http://apr.kbcsm. hr Abstract The study explores the concept of meaning in life among adult alcoholics and individuals from the general population, as well as its relationship with personality traits based on the Big Five model. Clinical and non-clinical samples have been compared to the construct of meaning in life. After examining the relationship between meaning in life and personality traits, we analyzed the contribution of personality traits in explaining the meaning in life for both groups. The clinical sample consisted of 76 treated alcoholics from the alcoholism ward of a psychiatric hospital in Zagreb, while the non-clinical sample consisted of 77 participants. The following questionnaires were applied: Purpose in Life Test, IPIP – 100, and a questionnaire on general data created exclusively for the purpose of this research. The results have shown significant differences in experience regarding meaning in life between the clinical and non-clinical sample, as individuals from the general population consider their life to be more meaningful. Furthermore, meaning in life had a significant positive relationship to extraversion, agreeableness, openness and conscientiousness and significant negative to neuroticism, in both groups. Extraversion and conscientiousness in the explanation of meaning in life were significant predictors in the general population, while extraversion and neuroticism were significant predictors among adult alcoholics, with neuroticism as a negative predictor. The results of this study, apart from new scientific findings, could contribute to the development of practical interventions and improvements in the treatment of persons with alcohol addiction.
Exploratory Visualization with Multiple Linked Views

Exploratory visualization enables the user to test scenarios and investigate possibilities. Through an exploration, the user may change various parameter values of a visualization system that in turn alters the appearance of the visual result. For example, the changesmade may update what information is being displayed, the quantity or resolution of the information, the type of the display (say) from scatter plot to line-graph. Furthermore, the user may generate additional windows that contain the visual result of the new parameters so they can compare different ideas side-by-side (these multiple views may persist such that the user can compare previous incarnations). Commonly these windows are linked together to allow further investigation and discovery, such as selection by brushing or combined navigation. There are many challenges, such as linking multiple views with different data, initializing the different views, indicating to the user how the different views are linked. This chapter provides a review of current multiple linked-view tools, methodologies and models, discusses related challenges and ideas, and provides some rudiments for coordination within a geovisualization context. The types and uses of coordination for exploratory visualization are varied and diverse, these ideas are underused in geovisualization and exploratory visualization in general. Thus, further research needs to occur to develop specific geovisualization reference models and extensible systems that incorporate the rich variety of possible coordination exploration ideas.
Chapter 1 Evolutionary System Supporting Music Composition

Evolutionary algorithms are heuristic techniques for findi ng approximate solutions of hard optimization and adaptation problems. Du e to their ability to create innovative solutions, evolutionary algorithms are very we ll suited for applications in the domain of art. In this paper the system supporting huma n composer in the process of music composition with the use of evolutionary al gorithm is presented. The architecture of the system as well as implemented algori thms and results of selected experiments will be presented.
THE SEIE-SCUT SYSTEMS FOR ACOUSTIC SCENE CLASSIFICATION USING CNN ENSEMBLE

In this report, we present our works concerning task 1b of DCASE 2019, i.e. acoustic scene classification (ASC) with mismatched recording devices. We propose a strategy of CNN ensemble for ASC. Specifically, an audio feature, such as Mel-frequency cepstral coefficients (MFCCs) and logarithmic filter-bank (LFB), is first extracted from audio recordings. Then a series of convolutional neural network (CNN) is built for obtaining CNN ensemble. Finally, classification result for each test sample is based on the voting of all CNNS contained in the CNN ensemble.
Development of a Laboratory Model for Automated Road Defect Detection

Potholes and bumps are responsible for a large number of accidents on roads, leading to the loss of lives and properties. Developing proactive and early detection measures will be an effective approach for reducing accidents, and a source of information for timely road maintenance. Consequently, a laboratory model of a road defect detection system is proposed using a combination of an Ultrasonic sensor, a Global Positioning System (GPS) and an alert system. The methodology used involved computing the best placement for the ultrasonic sensor, and developing an algorithm for detecting the presence/absence of bumps or potholes using the time taken to receive reflected pulse signals. This laboratory model is also capable of logging road profile information to a database where vehicle users and road maintenance agencies can access for planning route movement and prioritising road repairs.
INVESTIGATING THE EFFECTS OF BRAND IDENTITY ON CUSTOMER LOYALTY *

Today, brandisoneofthebasic elementsofthebusiness,an essentialand integral part ofthe marketing strategy in the world economy, brands marketing is placed in the heart of the business and mostly, it‟s hiddenvalue resides within the hearts and minds of customers, clients, and prospects thatattracts them. Besides selling the products, establishing a lasting relationship between the product and certain group of customer‟s is the main purpose of each organization, the extraction of this stable interaction brings about a kind of commitment and customer loyalty toward the product. Brands with unique identity and targets, by providing tangible and friendly feature, are able to make a rich and beneficial relationship with customer‟s and not only capture a part of their heart and mind, but also capture a part of consumer‟s daily life. Therefore, the present study has been performed to examine the impact of brand identity on the development of customer loyalty toward the brand in the industry of mobile phone brands. In the present research, brands of mobile phones such as Nokia, Sony, Samsung, Sony Ericsson, HTC and GLXhave been selected to study as the statistical population and sample of the research of 385 persons in the chief (metropolis) city of Rasht. This research has been a Descriptive and Causal study and has been made through Field method. The data resulted from the questionnaire, has been examined by applying statistical tests proportional to research hypotheses using Spss and Lisrel software. The research findings indicate that all hypotheses were confirmed but hypotheses of relationship between brand identity and perceived value, customer satisfaction and confidence in brand through its identification.
Hierarchical Alignment Trees : A Recursive Factorization of Reordering in Word Alignments with Empirical Results

A word alignment in machine translation (MT) usually represents a mapping between word positions in a source sentence and word positions in its target language translation. In current MT research, word alignments are interpreted as grouping constrains usually exploited to define translation equivalence relations between source and target phrases. Existing work [Zhang et al., 2008] has endeavored to compactly and recursively represent the translation equivalence relations (phrase pairs) defined by an arbitrary word alignment. However, the problem of factorizing the reordering aspects of word alignments into recursive tree-like representations has received little attention in the literature. In the present paper we contribute a polynomial-time algorithm for factorizing any given word alignment into a decorated tree representation (called Hierarchical Alignment Tree – HAT) such that the word alignment is uniquely determined by its HAT representation. A HAT is the maximal tree for its word alignment and every one of its internal nodes is decorated with a local transduction operator. The transduction operators provide an explicit, recursive factorization of reordering/distortion patterns in word alignments, thereby generalizing a range of existing work on factorizing permutations (the right-hand sides of synchronous grammar rules). As a first application of the HAT algorithm, we contribute an empirical study revealing a break-down into subclasses of HATs that are equivalent to subclasses of manual as well as automatic word alignments. We find that, on average, manual word alignments have far shallower HATs than their automatically constructed counterparts, but that both exhibit surprisingly many more complex alignment phenomena than reported by earlier work. Our empirical study highlights one application of HATs as a tool for analyzing and visualizing the recursive reordering properties of word alignments.
Macro-Micro Analytics : Background , Motivation , Advantages and Remaining Challenges

Disappointment with simple development formulas, such as the Washington Consensus, which have seemingly failed to spur strong inclusive growth in Latin America and Africa; recent evidence that fast growing parts of the world, such as East Asia, are experiencing upward trends in inequality; polarized debates on globalization and poverty, heightened by the 2008 global food and energy prices spike and the 2009 global financial crisis, have all led economists to a renewed attention to the relationship between macro (growth) and micro (poverty and distribution) issues.
Optimizing Compiler Design for SIMD Computer

Utilizing the resource of the processor to improve the execution efficiency of the program and to increase the flexibility of the code is the key problem of optimizing complier for SIMD processor. This paper introduces M language and Li-Shan Single Instruction Multiple Data(LS SIMD) computer architecture and presents optimization method and implementation method of the M compiler for SIMD coprocessor combining with modern compiler techniques. It includes assigning register combining single value and compressing code, etc. Experimental result shows that the M compiler can generate exact and high efficient target code.
System Demo for Transfer Learning from Vision to Language using Domain Specific CNN Accelerator for On-Device NLP Applications

Power-efficient CNN Domain Specific Accelerator (CNN-DSA) chips are currently available for wide use in mobile devices. These chips are mainly used in computer vision applications. However, the recent work of Super Characters method for text classification and sentiment analysis tasks using two-dimensional CNN models has also achieved state-of-the-art results through the method of transfer learning from vision to text. In this paper, we implemented the text classification and sentiment analysis applications on mobile devices using CNN-DSA chips. Compact network representations using one-bit and three-bits precision for coefficients and five-bits for activations are used in the CNN-DSA chip with power consumption less than 300mW. For edge devices under memory and compute constraints, the network is further compressed by approximating the external Fully Connected (FC) layers within the CNN-DSA chip. At the workshop, we have two system demonstrations for NLP tasks. The first demo classifies the input English Wikipedia sentence into one of the 14 ontologies. The second demo classifies the Chinese online-shopping review into positive or negative.
How Can Robot's Gaze Ratio and Body Direction Show an Awareness of Priority to the People With Whom it is Interacting?

When a person interacts with others who require different levels of authority, his/her gaze ratio and body direction for each person are different, e.g., a salesperson who turns to and looks at a very important person (VIP) more attentively than a member of that VIP's staff. In this study, we investigate the effects of gaze ratios and body directions with which social robots demonstrate an awareness of authority when they are simultaneously interacting with multiple persons. We develop a gaze-controller system for a social robot and experimentally investigate the effects of the gaze ratio and the body direction (priority oriented: body direction turns to a target and non-priority oriented: body direction does not turn to a target) when the robot salesperson is describing items to two persons who are playing different roles: a VIP and a follower (e.g., member of a VIP's staff). Our experiment results show that different advantages of gaze ratio and body direction demonstrate an awareness of priority. In the priority-oriented condition, participants more highly evaluated the gaze at 100:0 ratio between VIPs and followers. But, in the non-priority-oriented condition, the participants more highly evaluated the gaze ratios at 80:20 or 90:10. These results contribute to a gaze behavior design for social robots that interact with multiple persons who require different levels of priority.
A Scalable Risk-based Framework for Rigorous Autonomous Vehicle Evaluation

While recent developments in autonomous vehicle (AV) technology highlight substantial progress, we lack tools for rigorous and scalable testing. Real-world testing, the de facto evaluation environment, places the public in danger, and, due to the rare nature of accidents, will require billions of miles in order to statistically validate performance claims. We implement a simulation framework that can test an entire modern autonomous driving system, including, in particular, systems that employ deep-learning perception and control algorithms. Using adaptive sampling methods to accelerate rare-event probability evaluation, we estimate the probability of an accident under a base distribution governing standard traffic behavior. We demonstrate our framework on a highway scenario.
FRAGMENTING MEANING: CLARIFICATION ELLIPSIS AND NOMINAL ANAPHORA

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Reducing Odd Generation from Neural Headline Generation

The Encoder-Decoder model is widely used in natural language generation tasks. However, the model sometimes suffers from repeated redundant generation, misses important phrases, and includes irrelevant entities. Toward solving these problems we propose a novel source-side token prediction module. Our method jointly estimates the probability distributions over source and target vocabularies to capture the correspondence between source and target tokens. Experiments show that the proposed model outperforms the current state-of-the-art method in the headline generation task. We also show that our method can learn a reasonable token-wise correspondence without knowing any true alignment 1 .
Detecting and localizing multiple spoofing attackers in wireless network

Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Although the identity of a node can be verified through cryptographic authentication, conventional security approaches are not always desirable because of their overhead requirements. This paper proposes to use spatial information, a physical property associated with each node, hard to falsify, and not reliant on cryptography, as the basis for (1) detecting spoofing attacks; (2) determining the number of attackers when multiple adversaries masquerading as a same node identity; and (3) localizing multiple adversaries. The spatial correlation of received signal strength (RSS) inherited from wireless nodes is used to detect the spoofing attacks. Then the problem of determining the number of attackers as multiclass detection problem is formulated. Cluster-based mechanisms is developed to determine the number of attackers. When the training data is available, Support Vector Machines (SVM) method is used to further improve the accuracy of determining the number of attackers. In addition, integrated detection and localization system is used to localize the positions of multiple attackers. Keywords—Wireless network security, spoofing attack, attack detection, localization
LEVEL OF INTELLECTUAL CAPITAL DISCLOSURE ACROSS INDIAN AND PAKISTANI

The transition of economies from production to knowledge based has captivated the concentration of corporate practitioners. Theory of Resource Based View (RBV) argues that the future performance indicator for sustainable performance for any economy will no longer be physical and financial capital but that would be intangible assets, for instance, people and their knowledge. Corporate sector, now, is in an exploration of new accounting practices in terms of intangible assets reporting into their annual reports which may help them to follow the transition from financial capital to intellectual capital (IC) reporting. With this intent of the study, four leading companies from the sectors of automobiles, textiles and banking from two competing neighbouring countries, Pakistan and India, are being selected for the sample. In this discourse, this study is undertaken to find out the relative level of IC recording and reporting. A list of 65 items of IC is identified and content analysis is used to demonstrate the level of IC disclosure in both countries. The results of the study postulate that IC reporting in two countries is very low for a selected period of 2012-2013. Out of 65 items of IC, only 3 to 4 common items are being reported by a few companies; thus, suggesting that corporate practitioners need to emphasize more rigorously towards level of reporting for achieving the competitive positioning and sustainable performance of firms.
The Effect of the Interactive Functions of Whiteboards on Elementary Students’ Learning

In recent years, the interactive whiteboard (IWB) has been regarded as the most prominent information and communication technology auxiliary instruction device. It is touted as elevating the traditional teaching environment to a digital teaching environment because of its highly interactive features. The purpose of this study is to investigate elementary students’ perceptions of their teachers’ use of the different IWBs’ interactive functions in class and the effect of different IWB’s interactive uses on students’ learning attitudes. The findings showed that teachers’ use of the IWBs’ basic interactive function helps students develop positive learning attitudes, including enjoyment of learning, usefulness of IWB, and willingness to learn. However, such effects from the use of the IWB’s advanced interactive functions were not as clear, particularly the effects on usefulness of IWB and willingness to learn. Moreover, the students’ personal experience operating the IWB had an effect on their enjoyment of learning and willingness to learn. The article concludes by offering some recommendations concerning integrating the IWB into instruction.
Supplemental Material 3 D-SIS : 3 D Semantic Instance Segmentation of RGB-D Scans

Table 2: Anchor sizes used for region proposal on the ScanNet dataset [1]. Sizes are given in voxel units, with voxel resolution of ≈ 4.69cm Table 3 details the layers used in our detection backbone, 3D-RPN, classification head, mask backbone, and mask prediction. Note that both the detection backbone and mask backbone are fully-convolutional. For the classification head, we use several fully-connected layers; however, due to our 3D RoI-pooling on its input, we can run our entire instance segmentation approach on full scans of varying sizes. We additionally list the anchors used for the region proposal for our model trained on the ScanNet [1] and SUNCG [3] datasets in Tables 2 and 1, respectively. Anchors for each dataset are determined through k-means clustering of ground truth bounding boxes. The anchor sizes are given in voxels, where our voxel size is ≈ 4.69cm.
Are second-order approximations necessary to calculate prediction errors in empirical Bayes disease mapping ?

Mapping of small area mortality (or incidence) risks is a widely used technique in public health research. The standardized mortality ratio (SMR) is a common direct index of disease mortality, but it might be very imprecise in areas or counties with low population. Then, the use of models that borrow information from related (neighbouring) regions, smoothing the crude mortality risks, is inevitable. In this work, we focus on comparing prediction error estimators used in Empirical Bayes (EB) disease mapping with alternative proposals from the small area estimation literature, to check if second order approximations are necessary in the disease mapping framework. The well known Scottish lips cancer data is used for illustrative purposes. A simulation study is also conducted.