Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tale of Two Numbers

It is known that this infinite sum has a finite value, but nobody knows what that value is. For over a year, Nicely has been carrying out various computations involving twin, triple, and quadruple primes. One of his objectives has been to demonstrate that today’s PCs are just as effective as supercomputers for this kind of research. He was using half a dozen different computers in his work until March, when he added a machine based on Intel Corporation’s Pentium chip to his collection. In June, Nicely noticed inconsistencies among several different quantities he was computing. He traced them to the values he was getting for 1/p and 1/(p+2). Although he was using double precision, those values were accurate only to roughly single precision. He placed the blame at first on his own programs, then on the compiler and operating system, then on the bus and motherboard in his machine. By late October though, he was convinced that the difficulty was in the chip itself. On October 30, in an email message to several other people who had access to Pentium systems, Nicely said that he thought there was a bug in the processor’s floating-point arithmetic unit. One of the recipients of Nicely’s memo posted it on the CompuServe network. Alex Wolfe, a reporter for E E T i m e s, spotted the posting and forwarded it to Terje Mathisen. Terje Mathisen, a computer jock at Norsk Hydro in Oslo, Norway, has published articles on programming the Pentium, and has posted notes on the Internet about the accuracy of its transcendental functions. Within hours of receiving Wolfe’s query, Mathisen confirmed Nicely’s example, wrote a short, assembly-language test program, and on November 3, initiated a chain of Internet postings in the newsgroup c o m p . s y s . i n t e l about the FDIV bug. (FDIV is the floating-point divide instruction on the Pentium.) A day later, Andreas Kaiser of Germany posted a list of two dozen numbers whose reciprocals are computed to only single-precision accuracy on the Pentium. On November 7, the EE Times published the first news article on the bug. The headline on the front page story by Wolfe was “Intel Fixes a Pentium FPU Glitch”. Tim Coe is an engineer at Vitesse Semiconductor in Southern California. He has designed floating-point arithmetic units himself and saw in Kaiser’s list of erroneous reciprocals clues to how the Pentium designers had tackled the same task. He surmised, correctly it turns out, that the Pentium’s division instruction employs a technique known to chip designers as a radix-4 SRT algorithm. This produces two bits of quotient per machine cycle and thereby makes the Pentium twice as fast at division as previous Intel chips running at the same clock rate. Coe created a model that explained all the errors Kaiser had reported in reciprocals. He also realized that the relative errors were potentially even larger for division operations involving numerators other than 1. His model led him to a pair of seven-digit integers whose ratio,
Supplementary Information to: “A tale of two antennules: The performance of crab odor-capture organs in air and water”

In this supplement, we provide further details on the materials and methods used to obtain the results presented in the paper, “A tale of two antennules: The performance of crab odorcapture organs in air and water”. In what follows, we begin by providing the methodology used to measure flow on physical models of the crab antennules using particle image velocimetry. Further, we provide the details of the mathematical model and numerical method used to conduct the computational simulations, including how the experimental velocity fields were coupled with the model. Finally, we give details on how the presented results were standardized and the statistical analysis. Also, included in this supplement are movies of simulations which correspond to results presented in Fig. 4 in the paper.
Exploring Efficient Implementations of Deep Learning Applications on Embedded Platforms

The promising results of deep learning (deep neural network) models in many applications such as speech recognition and computer vision have aroused a need for their realization on embedded platforms. Augmenting DL (Deep Learning) in embedded platforms grants them the support to intelligent tasks in smart homes, mobile phones, and healthcare applications. Deep learning models rely on intensive operations between high precision values. In contrast, embedded platforms have restricted compute and energy budgets. Thus, it is challenging to realize deep learning models on embedded platforms. In this thesis, we define the objectives of implementing deep learning models on embedded platforms. The main objective is to achieve efficient implementations. The implementation should achieve high throughput, preserve low power consumption, and meet real-time requirements. The secondary objective is flexibility. It is not enough to propose an efficient hardware solution for one model. The proposed solution should be flexible to support changes in the model and the application constraints. Thus, the overarching goal of the thesis is to explore flexible methods for efficient realization of deep learning models on embedded platforms. Optimizations are applied to both the DL model and the embedded platform to increase implementation efficiency. To understand the impact of different optimizations, we chose recurrent neural networks (as a class of DL models) and compared its’ implementations on embedded platforms. The comparison analyzes the optimizations applied and the corresponding performance to provide conclusions on the most fruitful and essential optimizations. We concluded that it is essential to apply an algorithmic optimization to the model to decrease it’s compute and memory requirement, and it is essential to apply a memory-specific optimization to hide the overhead of memory access to achieve high efficiency. Furthermore, it has been revealed that many of the work understudy focus on implementation efficiency, and flexibility is less attempted. We have explored the design space of Convolutional neural networks (CNNs) on Epiphany manycore architecture. We adopted a pipeline implementation of CNN that relies on the on-chip memory solely to store the weights. Also, the proposed mapping supported both ALexNet and GoogleNet CNN models, varying precision for weights, and two memory sizes for Epiphany cores. We
Inversion of Longer‐Period OBS Waveforms for P Structures in the Oceanic Lithosphere and Asthenosphere

We performed waveform inversion of the P waveforms recorded by our BroadBand Ocean Bottom Seismometers (BBOBSs) deployed in the Northwestern Pacific. Consequently, the depth profile of the P velocity of the oceanic upper mantle, which has not been well resolved by previous surface wave or receiver function analyses, was revealed. We considered the azimuthal anisotropy in the lithosphere, which significantly improved the variance reduction from 34% to 44%. The resulting P model exhibited higher and lower velocities in the lithosphere and asthenosphere, respectively. The velocity contrast was found to be second/third of that observed in the previous S models; however the obtained model appeared to have some trade‐off with the VS structures in the vicinity of the source. We compared our P model with the previous S model obtained using our BBOBSs and obtained the VP/VS model. The resulting VP/VS model has two notable features. First, the lithosphere is characterized by a rapid increase in the VP/VS values with depth, which implies chemical stratification. Second, the VP/VS values in the vicinity of the lithosphere‐asthenosphere boundary (LAB) are larger than the synthetic values of any major upper mantle mineral predicted by considering the anharmonic effects, which suggests the effects of anelasticity or melt.
Cutting Process Modeling With An Absolutely Rigid Tool Without Vibrations

The physical processes that occur during metal cutting are very complex. Plastic strain and deletion of the processed material is accompanied by a significant increase in temperature and vibration, which makes it difficult to study the cutting process. This article gives and describes the ratios that underlie finite element modeling of metal cutting. It considers the case of rectangular orthographic cutt ing with the formation of flow chips, as the most generic type of processing. The obtained ratios can be applied to simulate most types of metalworking (turning, milling, dril ling, etc.) in order to study the process of plastic strain of the metal, study the dynamics of the cutting process, evaluate stresses in the cutting tool and the workpiece, and forecast residual potentials in the processed surface, cutting tool design, etc. Formulas that describe the main processes occurring when cutting metals have been derived: plastic strain of the material, fracture, thermal processes and friction between the chips, tool and workpiece. The formulas are obtained on the basis of fundamental relations: mass conservation, energy conservation, impulse law, balance equat ions, thermal conductivity; equations relating stresses and strains of the body. Great attention has been givento the model of the processed material. The behavior of a viscous elastoplastic material is considered. The correctness of the obtained ratios is confirmed by comparison with the results of field experiments conducted by other authors. The analysis of the existing methods of finite element cutting modeling is carried out: the Lagrangian formulation, Euler formulation and the Arbitrary Lagrangian-Eulerian (ALE) formulation. Criteria for the selection of methods are given depending on the current problem.
BUSINESS MODEL FRAMEWORKS IN IOT CONTEXT – A LITERATURE REVIEW

Employing the Internet of Things (IoT) in business changes the way in which value is offered to customers. To enable and ensure effective value exchange, proper business models are required. In this literature review study (n=56), business model frameworks created for the IoT context were evaluated. The results show that although most of the frameworks emphasize the ecosystemic nature of IoT, even they still largely do not describe the connections, dynamics, and causality between the business model components. While IoT as such does not necessarily need a specific business model, the ecosystemic nature of IoT is bound to influence the business model, thus making IoT business models more integrative, inter-relational, and complex. The results also suggest that the ecosystem-level co-creation of business models needs to be emphasized and studied further.
VISCOSITY SOLUTIONS TO A NEW PHASE-FIELD MODEL FOR MARTENSITIC PHASE TRANSFORMATIONS

We investigate a new phase-field model which describes martensitic phase transitions, driven by material forces, in solid materials, e.g., shape memory alloys. This model is a nonlinear degenerate parabolic equation of second order, its principal part is not in divergence form in multi-dimensional case. We prove the existence of viscosity solutions to an initial-boundary value problem for this model.
Redundancy Management of Multipath Routing In Heterogeneous WSN

In this paper we propose redundancy management of heterogeneous wireless sensor networks (HWSNs), utilizing multipath routing to answer user queries in the presence of unreliable and malicious nodes. The key concept of our redundancy management is to exploit the tradeoff between energy consumption vs. the gain in reliability, timeliness, and security to maximize the system useful lifetime. We formulate the tradeoff as an optimization problem for dynamically determining the best redundancy level to apply to multipath routing for intrusion tolerance so that the query response success probability is maximized while prolonging the useful lifetime. Furthermore, we consider this optimization problem for the case in which a voting-based distributed intrusion detection algorithm is applied to detect and evict malicious nodes in a HWSN. We develop a novel probability model to analyze the best redundancy level in terms of path redundancy and source redundancy, as well as the best intrusion detection settings in terms of the number of voters and the intrusion invocation interval under which the lifetime of a HWSN is maximized. We then apply the analysis results obtained to the design of a dynamic redundancy management algorithm to identify and apply the best design parameter settings at runtime in response to environment changes, to maximize the HWSN lifetime.
Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring. This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered. This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory. By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography. Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.
Privacy Preserving Top-k Spatial Keyword in Cloud Computing Using Biometric Identification

—With the rapid development of location-based services in mobile Internet, spatial keyword queries have been widely employed in various real-life applications in recent years. To realize the great flexibility and cost savings, more and more data owners are motivated to outsource their spatio-textual data services to the cloud. However, directly outsourcing such services to the untrusted cloud may arise serious privacy concerns. Existing studies primarily focus on the design of privacy-preserving schemes for either spatial or keyword queries, and they cannot be applied to solve the privacy preserving spatial keyword query problem. To address this problem, we present a novel privacy-preserving top-k spatial keyword query scheme. Biometric identification is a reliable and convenient way of identifying individuals. The widespread adoption of biometric identification requires solid privacy protection against possible misuse, loss, or theft of biometric data. Existing techniques for privacy-preserving biometric identification primarily rely on conventional cryptographic primitives.
On an efficient RSA public key encryption scheme

RSA is the most widely used public key scheme for secure communication. Aboud et.al [1], proposed an improved version of original RSA scheme. They generalized it so that it can be implemented in the general linear group on the ring of integers mod n. In the proposed scheme the original message and the encrypted message are h × h square matrices with entries in Z n as against the original RSA scheme which involves integer values. However, we identiﬁed a shortcoming of that scheme and proposed a new and efﬁcient RSA public key encryption scheme. We also propose a digital signature scheme based on the new scheme.
DRUMKIT TRANSCRIPTION VIA CONVOLUTIVE NMF

Audio to midi software exists for transcribing the output of a multi-mic’ed drumkit. Such software requires that the drummer uses multiple microphones to capture a single stream of audio for each kit piece. This paper explores the ﬁrst steps towards a system for transcribing a drum score based upon the input of a single mono microphone. Non-negative Matrix Factorisation is a widely re-searched source separation technique. We describe a system for transcribing drums using this technique presenting an improved gains update method. A good level of accuracy is achieved on on complex loops and there are indications the mis-transcriptions are for perceptually less important parts of the score.
Global Journal of Advanced Engineering Technologies and Sciences Adaptive Clustering in Multipath Routing for Intrusion Detection and Redundancy Management in Wireless Sensor Networks

In this paper we propose redundancy management of heterogeneous wireless sensor networks (HWSNs), utilizing multipath routing to answer user queries in the presence of unreliable and malicious nodes. The key concept of our redundancy management is to exploit the tradeoff between energy consumption vs. the gain in reliability, timeliness, and security to maximize the system useful lifetime. We formulate the tradeoff as an optimization problem for dynamically determining the best redundancy level to apply to multipath routing for intrusion tolerance so that the query response success probability is maximized while prolonging the useful lifetime. Furthermore, we consider this optimization problem for the case in which a voting-based distributed intrusion detection algorithm is applied to detect and evict malicious nodes in a HWSN. We develop a novel probability model to analyze the best redundancy level in terms of path redundancy and source redundancy, as well as the best intrusion detection settings in terms of the number of voters and the intrusion invocation interval under which the lifetime of a HWSN is maximized. We then apply the analysis results obtained to the design of a dynamic redundancy management algorithm to identify and apply the best design parameter settings at runtime in response to environment changes, to maximize the HWSN lifetime.
Buying Intention and Buying Behavior towards Chicken Meat in Pakistan: Empirical Evidence from a Consumer Survey

Lot of available literature discusses determinants of food consumption behavior. This study aims to provide an evidence of determinants of chicken meat consumption behavior, within a framework of the theory of planned behavior (TPB) in Pakistan. Data of the study are collected from 461 consumers through self-administered questionnaires. Multiple regression analysis is used to identify the factors that determine chicken meat buying intention and buying behavior of Pakistani consumers. The multiple regression analysis results indicate that attitude, and perceived behavioral control have positive and significant impact both on chicken meat buying intention and chicken meat buying behavior. However subjective norms failed to predict intention and behavior. The study is also limited with respect taking one meat type, single country and regression model used and thus the authors suggest use of SEM and experimental design to address the problems associated with regression models. This study contributes to and extends our understanding of the chicken meat buying behavior in the emerging consumer market of Pakistan.
Collecting Empirical Data About Hyperparameters for Data Driven AutoML

All optimization needs some kind of prior over the functions it is optimizing over. We used a large computing cluster to collect empirical data about the behavior of ML performance, by randomly sampling hyperparameter values and performing cross-validation. We also collected information about cross-validation error by performing some evaluations multiple times, and information about progression of performance with respect to training data size by performing some evaluations on data subsets. We present how we collected data, make some preliminary analyses on the surrogate models that can be built with them, and give an outlook over interesting analysis this should enable.
Vector Autoregressive Model to Forecast Malaysian Economic Growth

Abstract – This study presents a comparative study on univariate time series via the Autoregressive Integrated Moving Average (ARIMA) model and multivariate time series via Vector Autoregressive (VAR) model in forecasting economic growth in Malaysia. This study used monthly economic indicators price from January 1998 to January 2016. The aim is to evaluate a VAR and ARIMA model to forecast economic growth and to suggest the best time series model from an existing model for forecasting economic growth in Malaysia. The forecast performances of these models were evaluated based on an out-of-sample forecast procedure using an error measure, Mean Absolute Percentage Error (MAPE). The results revealed that the VAR model outperform ARIMA model in term of forecasting accuracy.
Governmental Surveillance - The balance between security and privacy

The increased digitalisation of society and recent developments in AI is laying the ground for surveillance capabilities of a magnitude we have not seen before. Surveillance can be conducted by several different actors in society, this project focuses on the Swedish police currently using a large ensemble of surveillance technologies. Earlier this year, significant legislative changes governing the police authorities use of digital surveillance were enacted. These changes mean that the police now have been given an extended mandate to use digital surveillance as part of their professional practice, which places demands on balanced decisions and informed responsibility. On the one hand, the police have an interest to use digital surveillance to increase efficiency and security in society; on the other hand, the police must balance their interests with citizen’s so-called integrity-interests and right to privacy. This study will therefore examine to what extent the Swedish Police Authority pay attention to questions such as integrity and privacy when introducing digital surveillance. The study is guided by the following questions: (i) What opportunities can be related to the implementation and use of digital surveillance in police work? (ii) What kind of challenges do the increasing use of digital surveillance create between organisational governance, police officers’ work practice, and the integrity of citizens - and how do the police tackle these challenges? Theoretically, we draw on the established research fields on surveillance and privacy and empirically this study is designed as a qualitative study of the Swedish Police as our main case.