nils-herrmann/clean_scirepeval huggingface dataset:
Low temperature Cu-Cu direct bonding for 3D-IC by using fine crystal layer

In this paper, we report a method of low temperature solid diffusion bonding. To investigate bondability of solid diffusion, we examined the effect of bump metals and bump planarization methods. Cu and Au bump were used for bump metals and CMP and ultra-precision cutting were used for bump planarization methods. We found that fine crystal layer could be formed on only cut Cu and Au bumps, and especially cut Cu bumps had a thick fine crystal layer on the surface. The layer on cut Cu bump was found to be easily to recrystallize at low temperature condition of 150 degree C. Moreover, the bonding interface of cut Cu bump disappeared at 200 degree C for 30 min, which means solid diffusion across the interface was realized with the contribution of fine crystal layer. In addition, for Cu-Cu direct bonding, formic acid treatment before bonding is effective because formic acid can react at low temperature without destroying fine crystal layer. That led to achieve high bonding strength between cut Cu bumps.
Poster Abstract: Resource Aware Placement of Data Stream Analytics Operators on Fog Infrastructure for Internet of Things Applications

While cloud computing led the path towards a revolutionary change in the modern day computing aspects, further developments gave way to the Internet of Things and its own range of highly interactive applications. While such a paradigm is more distributed in reach, it also brings forth its own set of challenges in the form of latency sensitive applications, where a quick response highly contributes to efficient usage and QoS (Quality of Service). Fog computing, which is the answer to all such challenges, is rapidly changing the distributed computing landscape by extending the cloud computing paradigm to include widespread resources located at the network edge. While the fog paradigm makes use of edge-ward devices capable of computing, networking and storage, one of the key impending challenges is to determine where to place the data analytic operators for maximum efficiency and least costs for the network and its traffic, the efficient algorithmic solution to which we seek to propose by way of this work underway.
On the vehicle stability control for electric vehicle based on control allocation

This paper proposes a vehicle stability control (VSC) scheme for a four-wheel-drive electric vehicle based on the control allocation techniques. The wheels of the electric vehicle are driven by the electric motor individually, which makes it more convenient to control tires forces and moments for stabilizing vehicle motion. But the over-actuated system is complex to control. The control allocation technique is well-known used in marine craft and aircraft to resolve over-actuated problems. By introduction of control allocation to electric vehicle control, a large degree of modularization of the different levels of control is obtained. A control analysis layer is designed using a sliding mode control, which specifies a desired yaw moment to satisfy the vehicle stability. And then the control allocation distributes this yaw moment among the individual wheels by commanding appropriate wheel longitudinal slip. Simulation results in the Carmakerreg environment have shown that the proposed control scheme takes advantages of electric vehicle and enhances the vehicle stability.
All-digital PLL and transmitter for mobile phones

We present the first all-digital PLL and polar transmitter for mobile phones. They are part of a single-chip GSM/EDGE transceiver SoC fabricated in a 90 nm digital CMOS process. The circuits are architectured from the ground up to be compatible with digital deep-submicron CMOS processes and be readily integrateable with a digital baseband and application processor. To achieve this, we exploit the new paradigm of a deep-submicron CMOS process environment by leveraging on the fast switching times of MOS transistors, the fine lithography and the precise device matching, while avoiding problems related to the limited voltage headroom. The transmitter architecture is fully digital and utilizes the wideband direct frequency modulation capability of the all-digital PLL. The amplitude modulation is realized digitally by regulating the number of active NMOS transistor switches in accordance with the instantaneous amplitude. The conventional RF frequency synthesizer architecture, based on a voltage-controlled oscillator and phase/frequency detector and charge-pump combination, has been replaced with a digitally controlled oscillator and a time-to-digital converter. The transmitter performs GMSK modulation with less than 0.5/spl deg/ rms phase error, -165 dBc/Hz phase noise at 20 MHz offset, and 10 /spl mu/s settling time. The 8-PSK EDGE spectral mask is met with 1.2% EVM. The transmitter occupies 1.5 mm/sup 2/ and consumes 42 mA at 1.2 V supply while producing 6 dBm RF output power.
Characterization and Modeling of Nonfilamentary Ta/TaOx/TiO2/Ti Analog Synaptic Device

A two-terminal analog synaptic device that precisely emulates biological synaptic features is expected to be a critical component for future hardware-based neuromorphic computing. Typical synaptic devices based on filamentary resistive switching face severe limitations on the implementation of concurrent inhibitory and excitatory synapses with low conductance and state fluctuation. For overcoming these limitations, we propose a Ta/TaOx/TiO2/Ti device with superior analog synaptic features. A physical simulation based on the homogeneous (nonfilamentary) barrier modulation induced by oxygen ion migration accurately reproduces various DC and AC evolutions of synaptic states, including the spike-timing-dependent plasticity and paired-pulse facilitation. Furthermore, a physics-based compact model for facilitating circuit-level design is proposed on the basis of the general definition of memristor devices. This comprehensive experimental and theoretical study of the promising electronic synapse can facilitate realizing large-scale neuromorphic systems.
A Tool to Assess the Comfort of Wearable Computers

Wearable computer comfort can be affected by numerous factors, making its assessment based on one value with one scale inappropriate. This paper presents a tool that measures wearable comfort across six dimensions: emotion, attachment, harm, perceived change, movement, and anxiety. The dimensions for these comfort rating scales were specifically developed for wearable equipment assessment by applying multidimensional scaling to a comfort term association matrix developed using the results of groupings of wearable computer comfort terms. Testing the scales on four different types of wearable computer showed that the scales can be used to highlight differences in comfort between different types of technology for different aspects of comfort. An intraclass correlation of .872 suggested that the scales were used with a high level of reliability. A second study showed that modifications made to a wearable computer resulted in improvements in comfort, although they were not significant (p > .05). A potential application for this research is as an aid to designers and researchers for assessing the wearability, in terms of comfort, of wearable computer devices and to determine the effectiveness of any modifications made to the design of a wearable device.
3D Printed Dielectric Reflectarrays: Low-Cost High-Gain Antennas at Sub-Millimeter Waves

Dielectric reflectarray antennas are proposed as a promising low-loss and low-cost solution for high gain terahertz (THz) antennas. Variable height dielectric elements are used in the reflectarray designs, which allow for the use of low dielectric-constant materials. Polymer-jetting 3-D printing technology is utilized to fabricate the antenna, which makes it possible to achieve rapid prototyping at a low-cost. Numerical and experimental results are presented for 3 different prototypes operating at 100 GHz, which show a good performance. Moreover the methodology proposed here is readily scalable, and with the current material and fabrication technology, designs up to 1.0 THz can be realized. This study reveals that the proposed design approach is well suited for low-cost high-gain THz antennas.
Reference scope identification for citances by classification with text similarity measures

This paper targets at the first step towards generating citation summaries - to identify the reference scope (i.e., cited text spans) for citances. We present a novel classification-based method that converts the task into binary classification which distinguishes cited and non-cited pairs of citances and reference sentences. The method models pairs of citances and reference sentences as feature vectors where citation-dependent and citation-independent features based on the semantic similarity between texts and the significance of texts are explored. Such vector representations are utilized to train a binary classifier. For a citance, once the set of reference sentences classified as the cited sentences are collected, a heuristic-based filtering strategy is applied to refine the output. The method is evaluated using the CL-SciSumm 2016 datasets and found to perform well with competitive results.
A Review of Thyroid Disorder Detection Using Medical Images

Thyroid is one of the largest endocrine gland. It is a small butterfly shaped gland which is located in the front portion of the neck.It is located just below the Adams apple. Thyroid gland produces hormones that help the body to control metabolism. There are various thyroid disorders. It includes Hyperthyroidism, Hypothyroidism, goiter and thyroid nodules (benign/malignant). Various modalities that are used to detect and classify abnormalities of the thyroid gland are Ultrasound imaging, Computer Tomography(CT),Magnetic Resonance Imaging(MRI) and Computer Aided Diagnosis (CAD).CAD help radiologists and doctors to increase the diagnosis accuracy, reduce biopsy ratio and save their time and effort. Medical image analysis has played an important role in many clinical procedures for detecting different types of human diseases.Thyroid medical images are utilized for the diagnosis process. KeywordsThyroid, Thyroid disorders, Medical imaging, Segmentation,Classification, SVM,KNN. __________________________________________________*****_________________________________________________
Cloud-assisted Industrial Internet of Things (IIoT) - Enabled framework for health monitoring

The promising potential of the emerging Internet of Things (IoT) technologies for interconnected medical devices and sensors has played an important role in the next-generation healthcare industry for quality patient care. Because of the increasing number of elderly and disabled people, there is an urgent need for a real-time health monitoring infrastructure for analyzing patients’ healthcare data to avoid preventable deaths. Healthcare Industrial IoT (HealthIIoT) has significant potential for the realization of such monitoring. HealthIIoT is a combination of communication technologies, interconnected apps, Things (devices and sensors), and people that would function together as one smart system to monitor, track, and store patients’ healthcare information for ongoing care. This paper presents a HealthIIoT-enabled monitoring framework, where ECG and other healthcare data are collected by mobile devices and sensors and securely sent to the cloud for seamless access by healthcare professionals. Signal enhancement, watermarking, and other related analytics will be used to avoid identity theft or clinical error by healthcare professionals. The suitability of this approach has been validated through both experimental evaluation, and simulation by deploying an IoT-driven ECG-based health monitoring service in the cloud.
Symbolic Music Genre Transfer with CycleGAN

Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have recently been applied to style and domain transfer for images, and in the case of VAEs, music. GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer. In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer. Evaluations using separate genre classifiers show that the style transfer works well. In order to improve the fidelity of the transformed music, we add additional discriminators that cause the generators to keep the structure of the original music mostly intact, while still achieving strong genre transfer. Visual and audible results further show the potential of our approach. To the best of our knowledge, this paper represents the first application of GANs to symbolic music domain transfer.
Limiting the Spread of Fake News on Social Media Platforms by Evaluating Users' Trustworthiness

Today’s social media platforms enable to spread both authentic and fake news very quickly. Some approaches have been proposed to automatically detect such “fake” news based on their content, but it is difficult to agree on universal criteria of authenticity (which can be bypassed by adversaries once known). Besides, it is obviously impossible to have each news item checked by a human. In this paper, we a mechanism to limit the spread of fake news which is not based on content. It can be implemented as a plugin on a social media platform. The principle is as follows: a team of factcheckers reviews a small number of news items (the most popular ones), which enables to have an estimation of each user’s inclination to share fake news items. Then, using a Bayesian approach, we estimate the trustworthiness of future news items, and treat accordingly those of them that pass a certain “untrustworthiness” threshold. We then evaluate the effectiveness and overhead of this technique on a large Twitter graph. We show that having a few thousands users exposed to one given news item enables to reach a very precise estimation of its reliability. We thus identify more than 99% of fake news items with no false positives. The performance impact is very small: the induced overhead on the 90th percentile latency is less than 3%, and less than 8% on the throughput of user operations.
DNNVM : End-to-End Compiler Leveraging Heterogeneous Optimizations on FPGA-based CNN Accelerators

The convolutional neural network (CNN) has become a state-of-the-art method for several artificial intelligence domains in recent years. The increasingly complex CNN models are both computationbound and I/O-bound. FPGA-based accelerators driven by custom instruction set architecture (ISA) achieve a balance between generality and efficiency, but there is much on them left to be optimized. We propose the full-stack compiler DNNVM, which is an integration of optimizers for graphs, loops and data layouts, and an assembler, a runtime supporter and a validation environment. The DNNVM works in the context of deep learning frameworks and transforms CNN models into the directed acyclic graph: XGraph. Based on XGraph, we transform the optimization challenges for both the data layout and pipeline into graph-level problems. DNNVM enumerates all potentially profitable fusion opportunities by a heuristic subgraph isomorphism algorithm to leverage pipeline and data layout optimizations, and searches for the optimal execution strategies of the whole computing graph. On the Xilinx ZU2 @330 MHz and ZU9 @330 MHz, we achieve equivalently state-of-the-art performance on our benchmarks by naive implementations without optimizations, and the throughput is further improved up to 1.26x by leveraging heterogeneous optimizations in DNNVM. Finally, with ZU9 @330 MHz, we achieve state-of-the-art performance for VGG and ResNet50. We achieve a throughput of 2.82 TOPs/s and an energy efficiency of 123.7 GOPs/s/W for VGG. Additionally, we achieve 1.38 TOPs/s for ResNet50.
Smart Home Automated Control System Using Android Application and Microcontroller

Smart Home System (SHS) is a dwelling incorporating a communications network that connects the electrical appliances and services allowing them to be remotely controlled, monitored or accessed. SHS includes different approaches to achieve multiple objectives range from enhancing comfort in daily life to enabling a more independent life for elderly and handicapped people. In this paper, the main four fields for SHS which are, home automation and remote monitoring, environmental monitoring, including humidity, temperature, fault tracking and management and finally the health monitoring have been considered. The system design is based on the Microcontroller MIKRO C software; multiple passive and active sensors and also a wireless internet services which is used in different monitoring and control processes .This paper presents the hardware implementation of a multiplatform control system for house automation and combines both hardware and software technologies. The system results shows that it can be classified as a comfortable, secure, private, economic and safe system in addition to its great flexibility and reliability.
A multimodal adaptive session manager for physical rehabilitation exercising

Physical exercising is an essential part of any rehabilitation plan. The subject must be committed to a daily exercising routine, as well as to a frequent contact with the therapist. Rehabilitation plans can be quite expensive and time-consuming. On the other hand, tele-rehabilitation systems can be really helpful and efficient for both subjects and therapists. In this paper, we present ReAdapt, an adaptive module for a tele-rehabilitation system that takes into consideration the progress and performance of the exercising utilizing multisensing data and adjusts the session difficulty resulting to a personalized session. Multimodal data such as speech, facial expressions and body motion are being collected during the exercising and feed the system to decide on the exercise and session difficulty. We formulate the problem as a Markov Decision Process and apply a Reinforcement Learning algorithm to train and evaluate the system on simulated data.
Deep Learning for Intelligent Video Analysis

Analyzing videos is one of the fundamental problems of computer vision and multimedia content analysis for decades. The task is very challenging as video is an information-intensive media with large variations and complexities. Thanks to the recent development of deep learning techniques, researchers in both computer vision and multimedia communities are now able to boost the performance of video analysis significantly and initiate new research directions to analyze video content. This tutorial will present recent advances under the umbrella of video understanding, which start from a unified deep learning toolkit--Microsoft Cognitive Toolkit (CNTK) that supports popular model types such as convolutional nets and recurrent networks, to fundamental challenges of video representation learning and video classification, recognition, and finally to an emerging area of video and language.
CNNPred: CNN-based stock market prediction using several data sources

Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework with specially designed CNNs, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next days direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of initial features. The evaluations show a significant improvement in predictions performance compared to the state of the art baseline algorithms.
Intrinsic Motivation in Museums : Why Does One Want to Learn ?

One often meets successful adults, professionals, or scientists who recall that their lifelong vocational interest was first sparked by a visit to a museum. In these accounts the encounter with a real, concrete object from a different world an exotic animal, a strange dress, a beautiful artifact is the kernel from which a n entire career of learning grew For others with an already developed curiosity about some field such as zoology, anthropology, or art, the museum provided an essential link in the cultivation of knowledge a place where information lo'it its abstractness and became concrete. In either case, many people ascribe powerful motivation to a museum visit, claiming that their desire to learn more about some aspect of the world was directly caused by it. Granted that these accounts of "crystallizing experiences" (Waiters and Gaidner 1986) attributed to museums might often be embellished and exaggerated in retrospect, i t would be rash to dismiss them entirely, for the fascination of museums seems to be a very real psychological phenomenon. The question rather becomes. How do museums motivate viewers to learn? Is there a unique, sui generis "museum experience" that helps viewers start on the long journey of learning? How do museums present information in a meaningful way, a way that deepens a person's experience and promotes further learning? To begin answering these questions, it will be useful to review what we know about human motivation in relation to learning. Children are born with a desire for knowledge, and some of the most stupendous feats of learning to walk, talk, get along with others, to take care of oneself are accomplished without seeming effort in the first few years of life. It would be difficult to see how a species as dependent on learning as we are could have
Treacher collins syndrome.

Treacher Collins syndrome is a genetic disorder resulting in congenital craniofacial malformation. Patients typically present with downslanting palpebral fissures, lower eyelid colobomas, microtia, and malar and mandibular hypoplasia. This autosomal dominant disorder has a variable degree of phenotypic expression, and patients have no associated developmental delay or neurologic disease. Care for these patients requires a multidisciplinary team from birth through adulthood. Proper planning, counseling and surgical techniques are essential for optimizing patient outcomes. Here the authors review the features, genetics, and treatment of Treacher Collins syndrome.
Advanced obfuscation techniques for Java bytecode

There exist several obfuscation tools for preventing Java bytecode from being decompiled. Most of these tools simply scramble the names of the identifiers stored in a bytecode by substituting the identifiers with meaningless names. However, the scrambling technique cannot deter a determined cracker very long. We propose several advanced obfuscation techniques that make Java bytecode impossible to recompile or make the decompiled program difficult to understand and to recompile. The crux of our approach is to over use an identifier. That is, an identifier can denote several entities, such as types, fields, and methods, simultaneously. An additional benefit is that the size of the bytecode is reduced because fewer and shorter identifier names are used. Furthermore, we also propose several techniques to intentionally introduce syntactic and semantic errors into the decompiled program while preserving the original behaviors of the bytecode. Thus, the decompiled program would have to be debugged manually. Although our basic approach is to scramble the identifiers in Java bytecode, the scrambled bytecode produced with our techniques is much harder to crack than that produced with other identifier scrambling techniques. Furthermore, the run-time efficiency of the obfuscated bytecode is also improved because the size of the bytecode becomes smaller after obfuscation.