Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Model Checking-Driven Fair Comparison of Dynamic Thermal Management Techniques Under Multithreaded Workloads

Dynamic thermal management (DTM) techniques are being widely used for attenuation of thermal hot spots in many-core systems. Conventionally, DTM techniques are analyzed using simulation and emulation methods, which are in-exhaustive due to their inherent limitations and cannot provide for a comprehensive comparison between DTM techniques owing to the wide range of corresponding design parameters. In order to handle the above discrepancies, we propose to use model checking, a state-space-based formal method, to model, evaluate, and compare DTM techniques across various functional and performance parameters. The suggested framework includes a modeling flow and a set of generic modules that realistically model many-core and DTM parameters like temperature, power, application, intercore communication and task migration, etc. For analysis purpose, the framework provides a common ground for comparing DTM techniques by formalizing DTM principles and performance parameters as a set of logical properties. These properties are verified for different task load configurations, e.g., multithreaded, malleable, and the applications which do not support migration. We analyze state-of-the-art central (c-) and distributed (d-) DTM techniques to demonstrate the generality and efficacy of our approach. Our formal analysis shows that the state-of-the-art cDTM technique performs better than dDTM in terms of achieving thermal stability, task migration, and communication overhead. We believe that conventional analysis methods do not facilitate such an exhaustive comparison among the DTM techniques.
Reliability Indices Evaluation of Distribution Networks for Automation

The world electric power systems are fast becoming smart, with the grid possessing capabilities that enable it self-heal in the case of device failures. If the Nigerian distribution networks are automated, it would go a long way in sustaining the ailing power grid. For automation to be carried out in the network, certain features would be enhanced through integration of high-speed, reliable and secure data communication networks to manage the complex power systems effectively and intelligently. This work presents the reliability evaluation of power distribution networks in Rivers State, Nigeria. Certain reliability indices were calculated using data obtained from Port Harcourt Electricity Distribution Company (PHEDC), Ahoada Business unit and Agogo Services, Egi Power Distribution network. The Performance, reliability and quality of service of the networks were investigated. The investigation revealed the system had an average availability of 64.44% for Ahoada and 74.10% for Egi as against the ASAI goal of 99.99%, this values were obtained for Egi Network despite the fact it was supplied by a private turbine signaling that to enhance distribution network reliability, increased investment in power production was required and as such an indigenous automated distribution system that can remedy the unreliable local distribution system was suggested.
Social Skill Validation at LinkedIn

The main mission of LinkedIn is to connect 610M+ members to the right opportunities. To find the right opportunities, LinkedIn needs to understand each member's skill set and their expertise levels accurately. However, estimating members' skill expertise is challenging due to lack of ground-truth. So far, the industry relied on either hand-created small scale data, or large scale social gestures containing a lot of social bias (e.g., endorsements). In this paper, we develop the Social Skill Validation, a novel framework of collecting validations for members' skill expertise at the scale of billions of member-skill pairs. Unlike social gestures, we collect signals in an anonymous way to ensure objectiveness. We also develop a machine learning model to make smart suggestions to collect validations more efficiently. With the social skill validation data, we discover the insights on how people evaluate other people in professional social networks. For example, we find that the members with higher seniority do not necessarily get positive evaluations compared to more junior members. We evaluate the value of social skill validation data on predicting who is hired for a job requiring a certain skill, and model using social skill validation outperforms the state-of-the art methods on skill expertise estimation by 10%. Our experiments show that the Social Skill Validation we built provides a novel way to estimate the members' skill expertise accurately at large scale and offers a benchmark to validate social theories on peer evaluation.
Vocabulary Pyramid Network: Multi-Pass Encoding and Decoding with Multi-Level Vocabularies for Response Generation

We study the task of response generation. Conventional methods employ a fixed vocabulary and one-pass decoding, which not only make them prone to safe and general responses but also lack further refining to the first generated raw sequence. To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation. Specifically, the dialogue input and output are represented by multi-level vocabularies which are obtained from hierarchical clustering of raw words. Then, multi-pass encoding and decoding are conducted on the multi-level vocabularies. Since VPN is able to leverage rich encoding and decoding information with multi-level vocabularies, it has the potential to generate better responses. Experiments on English Twitter and Chinese Weibo datasets demonstrate that VPN remarkably outperforms strong baselines.
Research of automatic Chinese word segmentation

Automatic Chinese word segmentation is the fundamental task of Chinese information processing. At present ambiguous phrase segmentation and proper name recognition are two obstacles to the performances of Chinese word segmentation systems. We apply a corpus-based method to extract various language phenomena from real texts, and combine a statistical model with rules in Chinese word segmentation, which has increased the precision of segmentation by improving ambiguous phrase segmentation and unknown word recognition, and finally, we describe a Chinese word segmentation system developed by Shanxi University.
Event Knowledge in Sentence Processing: A New Dataset for the Evaluation of Argument Typicality

In the NLP literature, the thematic fit estimation task is defined as the task in which a system has to predict how likely a candidate argument (e.g. cop) is to fit a given a verb-specific role (e.g. the agent of to arrest) (Santus et al., 2017). Because of the scarcity of benchmark datasets, thematic fit models are currently evaluated by measuring the correlation between their output and human ratings for isolated verb-filler pairs (Sayeed et al., 2016). However, such evaluation does not account for the dynamic nature of argument expectations: there is robust psycholinguistic evidence that human update their predictions on upcoming arguments during sentence processing, depending on the way other verb arguments are filled (Bicknell et al., 2010; Matsuki et al., 2011). Consider, for example, how the expectation for the patient of to check would change if we use journalist or mechanic as agents. In this paper we introduce DTFit (Dynamic Thematic Fit), a dataset of human ratings for verb-role fillers in a given event context, with the aim of providing a rigorous benchmark for context-sensitive argument typicality modeling. The dataset accounts for the plausibility of patient, instrument and location roles, given the agent and the predicate.
EARTHQUAKE ANALYSIS IN EAST JAVA, INDONESIA BETWEEN 1960 – 2017 USING MARKOV CHAIN MODEL

Earthquake occurrence in East Java has been analyzed using a Markov Chain Model. The catalog data comprised of earthquake events from East Java and its vicinity from 1960 until 2017. Data were classified into subduction earthquake and inland fault earthquake. After that, the data were declustered using Reasenberg (1985) algorithm for removing the aftershock. Spatial analysis was conducted by dividing the research area into nine regions. Probability transition matrices which give information about the highest probability transition of earthquake occurrence in each region were calculated for different magnitude thresholds. Furthermore, the Chi-Square test was applied to examine the independence between earthquakes occurrence. For seismic hazard analysis purpose, the temporal Markov chain analysis was employed by determining the active (1) and inactive (0) period in each region based on the occurrence of earthquakes with M ≥ 5 and depth d < 70 km. From the two-state probability transition, the mean duration of active and inactive states in each region have been obtained. Both spatial and magnitude analysis results inferred that subduction earthquake with M ≥ 4, M ≥ 5, and inland fault earthquake with M ≥ 3 exhibited strong first-order Markov property, i.e. there was a robust dependency between an earthquake occurrence and the successive occurrence. The mean duration of the inactive state in the research area varied from 2.5 until 13.5 years.
Interactive Latent Space for Mood-Based Music Recommendation

Author(s): Andjelkovic, Ivana | Advisor(s): Roads, Curtis | Abstract: The way we listen to music has been changing fundamentally in past two decades with the increasing availability of digital recordings and portability of music players. Up to date research in music recommendation attracted millions of users to online, music streaming services, containing tens of millions of tracks (e.g. Spotify, Pandora). The main focus of up to date research in recommender systems has been algorithmic accuracy and optimization of ranking metrics. However, recent work has highlighted the importance of other aspects of the recommendation process, including explanation, transparency, control and user experience in general. Building on these aspects, this dissertation explores user interaction, control and visual explanation of music related mood metadata during recommendation process. It introduces a hybrid recommender system that suggests music artists by combining mood-based and audio content filtering in a novel interactive interface. The main vehicle for exploration and discovery in music collection is a novel visualization that maps moods and artists in the same, latent space, built upon reduced dimensions of high-dimensional artist-mood associations. It is not known what the reduced dimensions represent and this work uses hierarchical mood model to explain the constructed space. Results of two user studies, with over 200 participants each, show that visualization and interaction in a latent space improves acceptance and understanding of both metadata and item recommendations. However, too much of either can result in cognitive overload and a negative impact on user experience. The proposed visual mood space and interactive features, along with the aforementioned findings, aim to inform design of future interactive recommendation systems.
Further Results on the VECD Operator and Its Application

In this paper, we consider the matrix vectorization operator termed the vecd operator, which has recently been introduced in the literature. This operator stacks up distinct elements of a symmetric matrix in a way that differs from that of the well-known vech operator; it stacks up not columns, but diagonals. We give further consideration to the vecd operator and related matrices, and derive their various useful properties. We provide some statistical applications of the vecd operator to illustrate its usefulness.
Toward Robust and Efficient Interpretations of Idiomatic Expressions in Context

Studies show that a large number of idioms can be interpreted figuratively or literally depending on their contexts. This usage ambiguity has negative impacts on many natural language processing (NLP) applications. In this thesis, we investigate methods of building robust and efficient usage recognizers by modeling interactions between contexts and idioms. We aim to address three problems. First, how do differences in idioms’ linguistic properties affect the performances of automatic usage recognizers? We analyze the interactions between context representations and linguistic properties of idioms and develop ensemble models that predict usages adaptively for different idioms. Second, can an automatic usage recognizer be developed without annotated training examples? We develop a method for estimating the semantic distance between context and components of an idiom and then use that as distant supervision to guide further unsupervised clustering of usages. Third, how can we build one generalized model that reliably predicts the correct usage for a wide range of idioms, despite of variations in their linguistic properties? We recast this as a problem of modeling semantic compatibility between the literal interpretation of an arbitrary idiom and its context. We show that a general model of semantic compatibility can be trained from a large unannotated corpus, and that the resulting model can be applied to an arbitrary idiom without specific parameter tuning. To demonstrate that our work can benefit downstream NLP applications, we perform a case study on machine translation. It shows that our model can help to improve the translation quality of sentences containing idioms.
Uses and Attitudes of Old and Oldest Adults towards Self-Monitoring Health Systems

Oldest adults (80 years and over) are the fastest growing group in the total world population. This is putting pressure on national healthcare budgets, as the distribution of healthcare expenses is strongly age-dependent. One way of mitigating this burden may be to let older adults contribute to their own health directly by using self-management health systems (SMHS). SMHS might help older, including oldest, adults gain insight into their health status, and invite them to take action. However, while many studies report on user evaluations of older adults with one specific sensor system, fewer studies report on older adults’ uses and attitudes towards integrated SMHS. Moreover, most studies include participants with mean ages of 65 rather than 80. In this paper, we report on a qualitative study, consisting of a focus group interview and a user evaluation of an SMHS by 12 participants with a median age of 85 years. Three main findings were derived: Older adults (1) showed heterogeneity in computer skills, (2) found health technologies useful for others – not yet for themselves, and (3) perceived health technologies as a threat to social interaction. These findings suggest that health technologies are not ready for adoption by older adults yet, and further research on making them more accessible and desirable is required.
A combined clustering and placement algorithm for FPGAs

One of the major drawbacks of reprogrammable microchips, such as field-programmable gate arrays (FPGAs), is an inherent speed disadvantage over ASIC technologies. To mitigate this speed disadvantage, this thesis presents a novel algorithm to improve timing performance at the possible expense of area and runtime. The algorithm presented leverages node duplication and a depth-optimal initial clustering to provide a starting point for a non-greedy, iterative optimization technique using detailed placement and timing information to develop the final clustering and placement solutions. For a set of benchmarks commonly used in FPGA research, the proposed algorithm achieves an 11% critical-path delay improvement compared to the VPR academic tool flow. This performance improvement is obtained at the expense of a 44% increase in area usage and a 26x increase in maximum runtime. Techniques have also been implemented to sacrifice performance to moderate the area or runtime increases. For a 1% critical-path delay penalty, the runtime can be improved by a factor of 4. The algorithm also provides facilities to impose area restrictions, in which case timing degradation is proportional to the area saved.
Supporting the design of online learning through use of learning designs

The PhD study outlined in this poster addresses the need for more targeted development of university teacher's integration of digital technologies into the design of their courses in higher education. Technology use and integration in a rapidly changing environment poses a challenge for university teachers and the current use of technology in higher education falls short of expectations. It has been suggested that the problem is due to the separation of technology from both pedagogy and content knowledge (Mishra & Koehler, 2006). This study investigates the use of learning designs to support the process of designing units of study and the development of knowledge around that process. Using a multiple case study approach, the study focuses on deepening understandings of technological, pedagogical and content knowledge throughout the design process and the identification of methods to support university teachers.
Workshop F. Network Computing Keynote Speech Some Research Projects On Clusters Of Personal Computers

Clusters of personal computers are becoming a viable, low-cost, alternative to traditional high-performance computing platforms as well as to more expensive networks of workstations. This is due in part to the better performance/cost ratio that characterizes personal computer hardware and in part to the outcome of various research projects aimed at producing high-speed communication systems out of of-the-shelf local area network technology PC clusters are starting to be used for commercial applications as well as in research environments. 1. International research projects
CCF: A Framework for Building Confidential Verifiable Replicated Services

We present CCF, a framework to build permissioned confidential blockchains. CCF provides a simple programming model of a highly-available data store and a universally-verifiable log that implements a ledger abstraction. CCF leverages trust in a consortium of governing members and in a network of replicated hardware-protected execution environments to achieve high throughput, low latency, strong integrity and strong confidentiality for application data and code executing on the ledger. CCF embeds consensus protocols with Byzantine and crash faulttolerant configurations. All configurations support strong service integrity based on the ledger contents. Even if some replicas are corrupt or their keys are compromised, they can be blamed based on their signed evidence of malicious activity recorded in the ledger. CCF supports transparent, programmable governance where the power of the consortium members is tunable and their activity is similarly recorded in the ledger for full auditability. We are developing an open-source implementation of CCF based on SGX-enabled Azure Confidential Compute, built on top of the Open Enclave SDK. Experimental results show that this implementation achieves throughput/latency tradeoffs up to 3 orders of magnitude better than previous confidential blockchain designs. Its code and documentation are available at https: //github.com/Microsoft/CCF.
Diachronic Sense Modeling with Deep Contextualized Word Embeddings: An Ecological View

Diachronic word embeddings have been widely used in detecting temporal changes. However, existing methods face the meaning conflation deficiency by representing a word as a single vector at each time period. To address this issue, this paper proposes a sense representation and tracking framework based on deep contextualized embeddings, aiming at answering not only what and when, but also how the word meaning changes. The experiments show that our framework is effective in representing fine-grained word senses, and it brings a significant improvement in word change detection task. Furthermore, we model the word change from an ecological viewpoint, and sketch two interesting sense behaviors in the process of language evolution, i.e. sense competition and sense cooperation.
Automated User Experience Monitor (A.U.X.M)

User experience is a crucial element in any kind of software. If the user cannot easily and satisfactorily use a software, they will not be inclined to use that software. Therefore, testing the user experience of a software is important. Conducting usability evaluations can be a tedious matter however, since there are several steps involved in setting up even a single usability test. This relates more to scheduling meetings between the test user and the expert evaluator that will conduct the test. Although there are several commercial software which allows designers to “relive”, or observe how users use their software, there are no such products that allow usability testing to be conducted, with the role of an expert evaluator being fulfilled. This paper describes a novel approach, the Automated User Experience Monitor (AUXM) system, where usability testing can be conducted in the absence of an expert evaluator, thus eliminating the issue of setting up meeting between test users, and expert evaluators. It is a system that allows designers/developers to have their user interfaces tested for usability. The designer of the prototype screens is able to feed their designs to the system described in this paper, and then invite any number of users to test the usability of those designs. While the users perform the tasks provided to them by the designer, their emotional feedback, keystrokes, mouse clicks, and verbal utterances are recorded. The recorded data is used by the system to inform the designers what went wrong, and suggest how to fix the issues in the designs. Results show that such a system is indeed possible to be implemented, and that usability analysis could be performed much easier using the system than having expert evaluators and test users meet in person. In conclusion, if the proposed approach is developed further, and refined, usability testing could change significantly in the software production industry.
Dissimilarity Measure Machines

This paper presents a dissimilarity-based discriminative framework for learning from data coming in the form of probability distributions. Departing from the use of positive kernel-based methods, we build upon embeddings based on dissimilarities tailored for distribution. We enable this by extending \citet{balcan2008theory}'s theory of learning with similarity functions to the case of distribution-shaped data. Then, we show that several learning guarantees of the dissimilarity still hold when estimated from empirical distributions. Algorithmically, the proposed approach consists in building features from pairwise dissimilarities and in learning a linear decision function in this new feature space. Our experimental results show that this dissimilarity-based approach works better than the so-called support measure machines or the sliced Wasserstein kernel, and that among several dissimilarities including Kullback-Leibler divergence and Maximum Mean Discrepancy, the entropy-regularized Wasserstein distance provides the best compromise between computational efficiency and accuracy.
Controversy and epistolary mediation in the construction of phonetic knowledge

As scientific knowledge grows, it often engenders controversy. The aim of the particular case of controversy studied here, in a series of letters exchanged between two phoneticians, is to capture vacillations of a discipline (phonetics) and its “actants” (phoneticians and their equipment), described at a key moment in the history of the discipline (an historical “turning point”), i.e. when phonetic knowledge was still governed by what one might call a “scientific regime of uncertainty”.