ABSTRACT. Corporate Governance, Business plan and Strategy and it’s alignment with IT investments has been a subject of discussion as early as 1990s. With productivity paradox and net present value (and other financial methods) of IT investments researched over a period of time, this paper is to put perspective for IT investment in present economic scenario with latest technologies and in digital world. With cloud computing infrastructure requiring all other IT governance principles and processes to be followed, IT investment demands investment more than only in hardware and software development, maintenance and support. IT investment and it’s relation to corporate strategy, business risk management, information economics, new business enabler, productivity tool, control systems to deliver shareholder value and constantly create business value has emerged to new domain. Innovation, it’s relation to technology, technological innovations and their relation to competitive advantage for an organization has created new areas that demand investment. With competition in business environment and integrated global economy, regulators demand more compliant business processes delivered through modern technological infrastructure. Having investigated the financial, non-financial, economic, tangible and non-tangible benefits of IT investment, this paper provides the new perspective to assess IT investments and make IT investment decision. This papers provides new perspectives and approaches for IT investment portfolio management.

ABSTRACT. One of the fundamental tasks when providing personalized tutoring services to learners in online learning systems is to predict learner performance on future exercises. To achieve this goal, it is necessary to estimate and trace the knowledge proficiency (KP) of learners by modeling their learning performance. The existing models either fail to capture the long-term dependencies in the exercising sequence to model the influence of a previous exercise to the current one or find it difficult to explain the results. To solve these issues, we propose herein a novel model, called the knowledge interaction-enhanced knowledge tracing (KIKT), to estimate and trace the evolution of learners' KP. We first propose a framework by unifying the strength of the memory network to enhance the representation of the knowledge state and the interpretability of the Item Response Theory to explain learner performance. In this framework, we trace each learner's KP on each knowledge concept over time, and further infer their proficiencies and the item characteristics using two kinds of neural networks. Moreover, we incorporate the knowledge interaction and the cognitive difficulty into our model to further exploit the long-term dependencies and the adaptive item difficulty in the exercising sequences. Extensive experiments conducted on five real-world datasets demonstrate the superiority of our model.

ABSTRACT. This paper empirically investigates the impact of human capital structure in the process of China's economic transition from a high-speed growth stage to a high-quality development stage. A high-quality development evaluation system has been built on the “Five development concept of innovation, harmonization, green, openness and sharing”. The measurement of human capital structure is calculated by proportion of employed population with various education. In the empirical analysis, static and dynamic panel data models are used, and the results of estimation reveal that increase in the proportion of high-educated population would effectively promote the quality of regional development. In order to avoid low quality-development trap, the less-developed regions in China should take corresponding policies to improve their human structures.

ABSTRACT. Semantic segmentation is a fundamental task in computer vision and is widely used in industry. However, current state-of-the-art architectures usually bring heavy computation complexity, making it hard to meet the demand for real-time, and can not be implemented in industry. In this paper, we propose a lightweight network to complete fast segmentation. Our network follows encoder-decoder style, which encodes rich spatial information at shallow layers and gains sufficient semantic information at deep layers. At decoder part, we use attention mechanism to re-weight features and gradually fuse high-level features back to low-level features. We evaluate our network on Cityscapes dataset. Our method achieves an accuracy of 68.0% mean intersection over union, and runs at 50.7 frames per second at full resolution (1024×2048) on one NVIDIA GeForce GTX 1080Ti card.

ABSTRACT. To solve the task of Chinese zero pronoun resolution, this paper proposes a Chinese zero pronoun resolution based on biaffine attention mechanism. First, Bert is employed to get representation of the text, and then build a candidate antecedent set for the target zero pronoun, and then get the representation of the local information and global information respectively for each antecedent candidate. Finally, biaffine attention mechanism is used in classification of the zero pronoun resolution. The Experiments on Chinese OntoNotes5.0 dataset show that our proposed approach can effectively improve the effect of Chinese zero pronoun resolution, and the F1 value reaches 59.7%.

ABSTRACT. Artificial Literature (ALit) starts seem possible with upcoming generative models. ALit consists of \textit{writing machines} that generates literary works. Although there are \textit{random machines} that imitates the language models, texts by the \textit{writing machine} should be far beyond, they need to have the structural similarity with the reference texts. In the framework for ALit, our first task is to find structure of tragedies which are very well stated beginning with Aristotle.

In this piece of work, the character networks are analyzed with graph theory in order to extract structural properties of Shakespearean texts. The character network is generated and represented as undirected weighted graphs. The weighted and betweenness centrality graphs are interpreted with and without protagonists/antagonists following the "Network Theory, Plot Analysis" by Franco Moretti.

As a conclusion, we investigated symmetries or antagonism clusters. There is an antagonism behind the protagonist/antagonists. This investigation is important to extract knowledge about the class or political struggle.

ABSTRACT. Unfair trades in a financial market makes participants to feel anxious and leads that the market does not works well. Therefore, most financial markets prohibit unfair trades. ``Spoofers'' places order which they has no intention to trade to manipulate market prices and to profit illegally. Most financial markets prohibit such orders, are called ``spoofing orders''. The question, however, how many orders a spoofer needs to manipulate market prices and to profit illegally is remain to be answered. Therefore, in this study, I built an artificial market mode (an agent-based model for financial markets) that imbalance of buy and sell orders also affects the expected returns and implemented the spoofer agent. I then investigated how many orders the spoofer needs to manipulate market prices and to profit illegally. The results indicate that showing spoofing orders more than waiting orders on the order book enables the spoofer to earn illegally, amplifies price fluctuation and makes the market inefficiency.

ABSTRACT. Opinion diffusion is often simulated in agent-based models to reveal the perpetuation of norms and beliefs. This paper presents a dual attitude model where agents’ interaction, information search, and opinion formation are influenced by the need for cognitive closure (NFCC). Two experiments simulated topic advocacy with either high- or low- NFCC agents. Experiment one initiated societies with unbiased distribution of NFCC levels between advocates of two competing topics, while experiment two initiated biased distribution of NFCC levels between the topics. Results in the unbiased condition showed that the popularity of the majority topic increases over time in high NFCC societies while it decreases over time in low NFCC societies. These results are reversed and magnified in the biased context where high NFCC agents provided an NFCC-advantage for their advocaed topic. When high NFCC agents’ advocated topic is the majority or equal at initiation, the topic’s popularity will increase significantly over time. When high NFCC agents’ advocated topic is minority at initiation, these agents resist the assimilative pressures of the majority topic to protect their own topic from popularity losses. Tracking simulations over time revealed starkingly different dynamics generated between the two experimental conditions, and showed the important roles low NFCC agents and edge-of-cluster agents play in enabling the emergence of such patterns. These results may shed light on the impact NFCC individuals have in within-society and between-societies cultural shifts.

ABSTRACT. Cultural differences in conformity pressures play a critical role in whether and how a society can effectively adopt a cooperative norm and fight against an evolving threat. Using an agent-based evolutionary game theoretic model, our results show that in general, tight societies with stronger conformity pressures adopt a cooperative norm faster than loose societies. As a consequence, the threat ends up lower in tight societies. However, high conformity pressures in tight societies are also a double-sided sword. Sometimes, a tight society may conform to a defective norm at the beginning of a threat, leading to a faster escalation of threat at the early stage of a threat. Nevertheless, as threat increases, tight societies are able to switch to a cooperative norm quickly and slow down the growth of threat, so eventually the threat levels in tight societies are close to or lower than that in loose societies. Our findings bring insight into how cultural differences in conformity pressures influence different societies’ success in dealing with collective threats.

ABSTRACT. Network collective emotions has obviously regional distribution differences on the macro-level, and it is closely related to the actual collective behavior. Therefore, it is necessary to understand the underlying influence factors. Studies have shown that individualism-collectivism significantly affects the spatial distribution of collective emotions, and itself affected by social and ecological factors such as pathogen prevalence. Based on this, we hypothesized that the pathogen prevalence affects collective happiness and sadness by collectivism. Using public archival data, existing collectivism regional index, and emotional expression data in Chinese micro-blog Weibo, we examine the impact of pathogen prevalence on the expression of happiness and sadness at the province-level in China. The results showed that the pathogen prevalence positively predicted the expression of sadness, and collectivism mediated this influence. However, we didn’t find that the pathogen prevalence suppressed the expression of happiness through collectivism. Finally, we discussed how the findings shed light on research concerning online emotion expression and possible future directions.

ABSTRACT. parasite disease theory of collectivism contends that inhabitants in regions with high prevalence of infectious diseases would adopt collectivism than those in regions with low prevalence in the long-term. It is not clear whether or not outbreak of infectious disease one time would elevate collectivism. Here using millions of Sina Weibo active users’ posts from January 20th, 2020 to February 16th, 2020, we constructed indicators of individualism and collectivism independently and found that the outbreak of COVID-19 increase collectivism and decrease individualism. Its theoretical contributions and implications to cultural psychology and big data are also discussed.

ABSTRACT. Digital screening and monitoring applications can aid providers in the management of behavioral health conditions. We explore deep language models for detecting depression, anxiety, and their comorbidity using input from conversational speech. Speech data comprise 16k spoken interactions labeled for both depression and anxiety. We find that results for binary classification range from 0.86 to 0.79 AUC, depending on condition and comorbidity. Best performance occurs for comorbid cases. We show that this result is not attributable to data skew. Finally, we find evidence suggesting that underlying word sequence cues may be more salient for depression than for anxiety.

ABSTRACT. This article brings together multidisciplinary re-search to present a new perspective on Alan Turing’s prediction:the ability of machines to imitate and deceive people.It has been supported with literature how emotional disordersin humans involved in the Turing Test, could affect the results.This work could open a new viewpoint on lines for helpingpeople with depression and anxiety through modern technologies.

ABSTRACT. One of the defining factors in human progress is the fact how humans have adopted technology into their everyday lives. One of these technologies that has seen a tremendous increase in usage is the mobile phone. The potential overuse of a smartphone device is very easily done, with many possible bad psychological side effects. Digital addiction is a form of addiction that has become more prevalent with people due to the ever-growing technological advances that our devices have achieved. Digital addiction can happen with any user who owns a digital device, and it is considered someone may be addicted when a user is using their device in an obsessive and compulsive manner. This work focuses on what could be done to assist people via a software application who either have the addiction or help prevent people from becoming addicted. This paper presents design and implementation of a mobile application to monitor and control the phone usage so that it can help combat digital addiction. The prototype implementation lets user see how much time they use on their phone as well as set some preferences. The study has been evaluated by user testing and having user feedback.

ABSTRACT. This paper extends the previous work in the field of persuasive technology as it aims to examine the solutions of interest that have been developed based on cultural preferences through applying persuasive strategies on tailored persuasive messages. We attempted to apply persuasive strategies to examine its effectiveness in changing users’ behaviour from a cultural perspective. This attempt was achieved by applying the principles of social influence that the targeted culture preferred on the tailored messages of the mobile health (mHealth) application. A between-group design was adopted in this study, where each group received weekly feedback intervention messages related to monitoring diabetes via the mHealth application. The control group received the original weekly messages, whereas the intervention group received the modified version of the original weekly messages. The data were collected through monitoring participants with diabetes type II for 16 weeks. The results indicated that the weekly feedback intervention messages were not suitable for the targeted audience as participants in both groups did not notice the messages. However, other results that focused on users’ engagement allowed us to understand the users’ behaviour and their level of engagement with the mHealth application.

ABSTRACT. The ahadith (plural of hadith), prophetic traditions for the Muslims around the world, are narrations originating from the sayings and the deeds of Prophet Muhammad (pbuh). They are considered one of the fundamental sources of Islamic legislation along with the Quran. The list of persons involved in the narration of each hadith is carefully scrutinized by scholars studying the hadith, with respect to their reputation and authenticity of the hadith. This is due to the ahadith’s legislative importance in Islamic principles. There were many narrators who contributed to this responsibility of preserving prophetic narrations over the centuries. But to date, no systematic and comprehensive study, based on the social network, has been adapted to understand the contribution of early hadith narrators and the propagation of hadith across generations. In this study, we represented the chain of narrators of the hadith collection from Sahih Bukhari as a social graph. Based on social network analysis (SNA) on this graph, we found that the network of narrators is a scale-free network. We identified a list of influential narrators from the companions (e.g., Abu Hurairah, Ibn Abbas, Ibn Umar, etc.) as well as the narrators from the second and third-generation (e.g., Shu'bah bin al-Hajjaj, Az-Zuhri, Sufyan bin 'Uyaynah, Sufyan bin Sa‘id Ath-Thawri, etc.) who contribute significantly in the propagation of hadith collected in Sahih Bukhari. We discovered sixteen communities from the narrators of Sahih Bukhari. In each of these communities, there are other narrators who contributed significantly to the propagation of prophetic narrations. We also found that most narrators were centered in Makkah and Madinah (in today’s Saudi Arabia) in the era of companions and, then, gradually the center of hadith narrators shifted towards Kufa, Baghdad (in today’s Iraq) and central Asia (e.g., Uzbekistan, Turkmenistan) over a period of time. To the best of our knowledge, this the first comprehensive and systematic study based on SNA, representing the narrators as a social graph to analyze their contribution to the preservation and propagation of hadith.