Miracle-dz/UniFAR-FTUs huggingface dataset:
Alternating Blind Identification of Power Sources for Mobile SoCs

The need for faster Systems on Chip (SoCs) has accelerated scaling trends, leading to a considerable power density increase and raising critical power and thermal challenges. The ability to measure power consumption of different hardware units is essential for the operation and improvement of mobile SoCs, as well as the enhancement of the power efficiency of the software that runs on them. SoCs are usually enabled with embedded thermal sensors to measure the temperature at the hardware unit level; however, they lack the ability to sense the power. In this paper we introduce an Alternating Blind Identification of Power sources (Alternating-BPI), a technique that accurately estimates the power consumption of individual SoC units without the use of any design based models. The proposed technique uses a novel approach to blindly identify the sources of power consumption, by relying only on the measurements from the embedded thermal sensors and the total power consumption. The accuracy and applicability of the proposed technique was verified using simulation and experimental data. Alternating-BPI is able to estimate the power at the SoC hardware unit level with up to 98.1% accuracy. Furthermore, we demonstrate the applicability of the proposed technique on a commercial SoC and provide a fine-grain analysis of the power profiles of CPU and GPU Apps, as well as Artificial Intelligence (AI), Virtual Reality (VR) and Augmented Reality (AR) Apps. Additionally, we demonstrate that the proposed technique could be used to estimate the power consumption per-process by relying on the estimated per-unit power numbers and per-unit hardware utilization numbers. The analysis provided by the proposed technique gives useful insights about the power efficiency of the different hardware units on a state-of-the-art commercial SoC.
Fast Detection of Snakes and Emotional Faces in the Macaque Amygdala

Primate vision is reported to detect snakes and emotional faces faster than many other tested stimuli. Because the amygdala has been implicated in avoidance and emotional behaviors to biologically relevant stimuli and has neural connections with subcortical nuclei involved with vision, amygdalar neurons would be sensitive to snakes and emotional faces. In this study, neuronal activity in the amygdala was recorded from Japanese macaques (Macaca fuscata) during discrimination of eight categories of visual stimuli including snakes, monkey faces, human faces, carnivores, raptors, non-predators, monkey hands, and simple figures. Of 527 amygdalar neurons, 95 responded to one or more stimuli. Response characteristics of the amygdalar neurons indicated that they were more sensitive to the snakes and emotional faces than other stimuli. Response magnitudes and latencies of amygdalar neurons to snakes and monkey faces were stronger and faster than those to the other categories of stimuli, respectively. Furthermore, response magnitudes to the low pass-filtered snake images were larger than those to scrambled snake images. Finally, analyses of population activity of amygdalar neurons suggest that snakes and emotional faces were represented separately from the other stimuli during the 50–100 ms period from stimulus onset, and neutral faces during the 100–150 ms period. These response characteristics indicate that the amygdala processes fast and coarse visual information from emotional faces and snakes (but not other predators of primates) among the eight categories of the visual stimuli, and suggest that, like anthropoid primate visual systems, the amygdala has been shaped over evolutionary time to detect appearance of potentially threatening stimuli including both emotional faces and snakes, the first of the modern predators of primates.
Beyond Just Safety: Delay-aware Security Monitoring for Real-time Control Systems

Modern embedded real-time systems (RTS) are increasingly facing more security threats than the past. A simplistic straightforward integration of security mechanisms might not be able to guarantee the safety and predictability of such systems. In this article, we focus on integrating security mechanisms into RTS (especially legacy RTS). We introduce Contego-C, an analytical model to integrate security tasks into RTS that will allow system designers to improve the security posture without affecting temporal and control constraints of the existing real-time control tasks. We also define a metric (named tightness of periodic monitoring) to measure the effectiveness of such integration. We demonstrate our ideas using a proof-of-concept implementation on an ARM-based rover platform and show that Contego-C can improve security without degrading control performance.
Sampling-based Label Propagation for Balanced Graph Partitioning

In this experience paper, we present new sampling-based algorithms for balanced graph partitioning based on the Label Propagation (LP) approach. The purpose is to define new heuristics to extend the multi-objective and scalable Balanced GRAph Partitioning algorithm B-GRAP proposed in \citeELMOUSSAWI_Dexa2020. The main challenge is related to how to build a graph sample that ensures stability and improves the convergence and the partitioning quality which depend strongly on the structure of the graph. We defined two sampling-based heuristics named RD-B-GRAP and HD-B-GRAP in order to study the behavior of the propagation according to different quality measures related to the vertex and the edge balance, to the edge cut, and also to the propagation time. The results obtained on different graphs showed that the sampling-based algorithms improve the propagation time without affecting the balance between partitions. Moreover, The edge cut is slightly improved on some graphs.
Nanosecond pulsed power technology for transient plasma generation

In this chapter we present a design approach of a high-voltage single-line nanosecond pulse source by using a thorough analytical investigation and by verification with 3D EM simulations in CST Microwave Studio. Through detailed analysis and simulation we arrive at a design for a fast rise time, variable pulse duration pulse source which is able to produce 1–10-nanosecond pulses with tens of kilovolt amplitude and less than hundreds of picosecond rise time. The variable pulse duration is achieved in two ways: i) by varying the length of the pulse forming line of the pulse source by using telescopic antennas, and ii) by realising a high-ohmic termination of the pulse forming line by means of magnetic material in the pulse forming line. Finally, we present the design of a first implementation of a 5-nanosecond pulse source with an oil spark gap for very fast switching.
A Lightweight Neural Network for Energy Disaggregation Employing Depthwise Separable Convolution

Non-Intrusive Load Monitoring (NILM) is a blind source separation problem which aims to estimate the electricity usage of individual appliances by decomposing a household’s aggregate electricity consumption. Recent state-of-the-art neural network models have delivered a good performance on load disaggregation, however these models have tremendous model size with huge numbers of parameters to tune and require large amounts of data for training, thus are computation and memory demanding, which makes it difficult to be practically implemented. To address this problem, we study the mechanism of depthwise separable convolution and propose a modified convolutional neural network model, which results in a lightweight NILM algorithm with the aim to be deployed on edge devices with limited computation power as mobile phones or smart meters. The lightweight NILM algorithm is evaluated with public available dataset: UKDALE. Assessment results prove that the proposed model delivers acceptable disaggregation performance with dramatically reduced model size and computation requirements.
Beyond Bernoulli: Generating Random Outcomes that cannot be Distinguished from Nature

Recently, Dwork et al. (STOC 2021) introduced Outcome Indistinguishability as a new desideratum for binary prediction tasks. Outcome Indistinguishability (OI) articulates the goals of prediction in the language of computational indistinguishability: a predictor is Outcome Indistinguishable if no computationally-bounded observer can distinguish Nature’s outcomes from outcomes that are generated based on the predictions. In this sense, OI suggests a generative model for binary outcomes that cannot be refuted given the empirical evidence and computational resources at hand. In this work, we extend Outcome Indistinguishability beyond Bernoulli, to outcomes that live in a large discrete or continuous domain. While the idea of OI for non-binary outcomes is natural for many applications, deﬁning OI in generality is not simply a syntactic exercise. We introduce and study multiple deﬁnitions of OI— each with its own semantics—for predictors that completely specify each individuals’ outcome distributions, as well as predictors that only partially specify the outcome distributions through statistics, such as moments. With the deﬁnitions in place, we provide learning algorithms for producing OI generative outcome models for general random outcomes. Finally, we study the relation of Outcome Indistinguishability and Multicalibration of statistics (beyond the mean) and relate our ﬁndings to the recent work of Jung et al. (COLT 2021) on Moment Multicalibration. We ﬁnd an equivalence between Outcome Indistinguishability and Multicalibration that is more subtle than in the binary case and sheds light on the techniques employed by Jung et al. to obtain Moment Multicalibration.
Tight Formulations of Unit Commitment with General Start-Up Ramp Rate Constraints

Unit commitment (UC) is a central market clearing process in wholesale electricity markets. With the increasing size and complexity of market-clearing models, UC problems become more and more complicated. To improve the computation efficiency, this paper tightens UC formulations with the general start-up ramp rate. Through analyzing their parameters over three periods, units are categorized into several categories for constructing corresponding tight formulations, and three of the categories are beyond existing works. By applying a recent systematic formulation tightening approach on top of these categories, new tight constraints of the general start-up ramp rate have been obtained. Numeral testing results illustrate the tightening process and demonstrate the improvement of computational efficiency when using our tightened formulations.
Are Wearables Good or Bad for Society? An Exploration of Societal Benefits, Risks and Consequences of Augmented Reality Smart Glasses.

: Recent market research forecasts predict that a new form of wearable devices will soon influence the media landscape: Augmented Reality Smart Glasses. While prior research highlights numerous potentials in personal and professional settings of smart glasses, this technology has also triggered several controversies in public discussions, for example, the risk of violating privacy and copyright laws. Yet, little research addresses the questions of whether smart glasses are good or bad for societies, and if yes, why. This study conducts exploratory research to contribute to narrowing this gap. Based on a survey among consumers, we identify several societal benefits and risks that determine consumers’ evaluation of the anticipated and desired success of smart glasses. These findings lead to numerous important implications for consumers, scholars, managers, and policy makers.
Evaluating the effectiveness of team building training : a case study of a management team undergoing team building training

Team building training within companies and organisations has become highly useful and practically essential for businesses to be successful as it helps colleagues understand each other better and maximise their full potential. The aim of this study was to establish a better understanding of the effectiveness of team building training, by evaluating the team building process of a Maltese management team. A qualitative approach was adopted: a sample of eight participants responded to a semi-structured interview. Thematic analysis resulted in 25 basic themes that were organised under four major themes, namely: the team building process; the team’s weaknesses; aims for training and outcomes of training. The findings suggest that team building training is effective in improving team-bond, communication, role-clarification and common direction.
Identifying the Bias: Evaluating Effectiveness of Automatic Data Collection Methods in Estimating Details of Bus Dwell Time

Data from automated vehicle location (AVL) systems, automatic passenger counter (APC) systems, and fare box payments have been heavily used to generate dwell time models with the goal of recommending improvements in efficiency and reliability of bus transit systems. However, automatic data collection methods may result in a loss of detail with regard to the dynamics of passenger activity, which may bias the estimates associated with dwell or passenger activity time. The purpose of this study is to understand better any biases that might exist from using data from AVL–APC systems or fare box payments when estimating dwell time. Manually collected data from Montreal, Quebec, Canada, are used to estimate detailed dwell time models. This study compared those estimates to models generated by using data similar to what was reported by AVL–APC systems and fare boxes. The results reveal an overestimation in the passenger activity component of dwell time, which is mainly attributed to excess dwell time that AVL–APC data and fare box payments generally do not capture. While AVL–APC and fare box technologies provide transit agencies with rich data for analysis, adjustments to such data collection methods are warranted to reduce the overestimation of dwell time and to provide a more accurate picture of what is happening on the ground to generate better interventions that can reduce dwell times.
A Hierarchical and Reconfigurable Process Element Design for Quantized Neural Networks

Convolution neural networks are very popular for various applications. However, data size and accuracy are the two major concerns to perform efficient and effective computations. In conventional CNN models, 32bits data are frequently used to maintain high accuracy. However, performing a bunch of 32bits multiply-and-accumulate (MAC) operations causes significant computing efforts as well as power consumptions. Therefore, recently researchers develop various methods to reduce data size and speed up calculations. Quantization is one of the techniques which reduces the number of the bits of data and the computational complexity at the cost of accuracy loss. To provide better computation effort and accuracy trade-off, different bitwidth may be applied to different layers within a CNN model. Therefore, a flexible Processing Element (PE) which can support operations of different bitwidth is in demand. In this paper, we propose a hierarchal PE structure that can support 8bits x 8bits, 8bits x 4bits, 4bits x 4bits and 2bits x 2bits operations. The structure applies the concept of reconfiguration and can avoid the redundant hardware for reconfiguration. Moreover, the concept of pipelining is also adopted in our design to provide better efficiency. The experimental results show that in 2bits x 2bits PE, we can achieve area reduction by 57% and 68% compared to a Precision-Scalable accelerator and Bit Fusion, respectively.
What do K-12 students feel when dealing with technology and engineering issues? Gardner's multiple intelligence theory implications in technology lessons for motivating engineering vocations at Spanish Secondary School

ABSTRACT The interest on engineering and scientific studies can be raised up even from the early years of academic instructional process. This vocation may be linked to emotions and aptitudes towards technological education. Particularly, students get in touch with these technological issues (namely STEM) during the Compulsory Secondary Education in Spain (12–16 years old). This work presents a preliminary evaluation of how relevant is Gardner's multiple intelligence theory (MIT) in the teaching–learning process within the Technology Lessons. In this sense, MIT was considered as an explanation variable of the emotional response within the different educational parts (so-called syllabus units, SU) in the Technology spanish curriculum. Different intelligence style (IS) will orient the student to a vision of the engineering and technology. This work tries to identify which relationships can be established between IS and specific technology and engineering learning. This research involved up to 135 students were subsequently tested about their predominant (IS) and on the emotions that arouse in them when working with each SU. The results were statistically significant and only those with a Logic-arithmetic or Environmental IS were not affected by the SU.Best teaching and learning practicesare required for encouraging further engineering studies.
Understanding Simultaneous Train and Test Robustness

This work concerns the study of robust learning algorithms. In practical settings, it is desirable to achieve robustness to many different types of corruptions and shifts in the data distribution such as defending against adversarial examples, dealing with covariate shifts, and contamination of training data (data poisoning). While there has been extensive recent work on these topics, models and algorithms for these different notions of robustness have been largely developed in isolation. In this paper, we propose a natural notion of robustness that allows us to simultaneously reason about train-time and test-time corruptions, that can be measured using various distance metrics (e.g., total variation distance, Wasserstein distance). We study our proposed notion in three fundamental settings in supervised and unsupervised learning (of regression, classiﬁcation and mean estimation). In each case we design sample and time-efﬁcient learning algorithms with strong simultaneous train-and-test robustness guarantees. In particular, our work shows that the two seemingly different notions of robustness at train-time and test-time are closely related, and this connection can be leveraged to develop algorithmic techniques that are applicable in both the settings.
EFFECT OF GREEN PACKAGING ON BUSINESS PERFORMANCE IN THE MANUFACTURING IN NAIROBI COUNTY, KENYA

The main purpose of the study is to determine effect of green packaging on firm performance in manufacturing in Nairobi County, Kenya. The study was informed by institutional theory and the resource-based theory. The study adopted the explanatory research design. A census of 133 firm managers working for 47 firms in Nairobi County. Data was gathered from respondents using questionnaires as data collection instruments. Cronbach alpha was used to measure the internal reliability of the structured questionnaire. Data was analyzed using descriptive statistics which included means and standard deviations, also multiple regression models were used to analyze data in order to determine the hypotheses for the study. Data was presented using tables. The results indicated that green packaging is key determinants of business performance in the manufacturing in Kenya. Thus firm managers should package their products in recyclable materials.
An enhanced cascade-based deep forest model for drug combination prediction

Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.
Measurements of Spatial and Temporal Variations in Ice Impact Pressures

This paper presents results of laboratory experimen ts involving a 1 m diameter ice cone colliding with an instrumented panel (Impact Module ). The tests were performed using a double pendulum apparatus that provided high impact energy. Images taken with a high speed camera at 500 fps from within the Impact Module pro vide information about the true contact area and pressure distribution during the collision . The acquired images were translated into pressure distributions using a specifically develop d software routine. These data are compared to the output of a separate pressure senso r located in the centre of the Impact Module. The images show increasing contact area as the collision proceeds and capture sudden losses of area as ice fracture and spalling occurred. Observations such as significant spalling events in the vicinity of the central pres sure sensor are also reflected in its recordings. The results provide maps of the temporal and spatia l variations in contact pressure, contact area and force as an ice impact progresses at speed s typical of impacts that might occur with icebreaking ships. INTRODUCTION In 2009 a first series of preliminary impact drop tests were performed on the Impact Module. The tests involved freshwater ice blocks of different masses (up to 130 kg) that were dropped from different heights. A detailed description of the tests and some first results are given in Gagnon et al. (2009). In another study ice crushing tests were performed that were quasi-static in nature and were achieved by pushing a 1 m diameter ice cone into the Impact Module. Some preliminary results of these tests were published in Gudimetla et al. (2012). In both studies the quick response time of the Impact Module to spalling events by showing real-time contact areas during the crushing event has been proven; a feature that makes it preferable to various alternative technologies with longer reaction times, especially considering the extremely fast fracture and spalling events during ice breaking. Here we are reporting on the continuation of tests in more realistic conditions. Large dynamic impact tests have been performed using a unique double pendulum apparatus. The tests were performed in the Structure’s lab at Memorial University of Newfoundland, St. John's, Canada. POAC’15 Trondheim, Norway Proceedings of the 23rd International Conference on Port and Ocean Engineering under Arctic Conditions June 14-18, 2015 Trondheim, Norway